Patent Publication Number: US-2023139606-A1

Title: Precision height estimation using sensor fusion

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application claims the benefit of U.S. Provisional Patent Application 63/274,448, filed on Nov. 1, 2021, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The disclosure generally relates to estimating heights of aerial robots and, more specifically, to robots that use different sensors to estimate heights accurately. 
     BACKGROUND 
     For aerial robots such as drones to be autonomous, aerial robots need to navigate through the environment without colliding with objects. Estimating the height of the robot at any time instance is important for the robot&#39;s navigation and collision avoidance, especially in an indoor setting. Conventionally, an aerial robot may be equipped with a barometer to determine the pressure change in various altitudes in order for the aerial robot to estimate the height. However, the measurements obtained from the barometer are often not sensitive enough to produce highly accurate height estimates. Also, pressure change in an indoor setting is either insufficiently significant or even unmeasurable. Hence, estimating heights for aerial robots can be challenging. 
     SUMMARY 
     Embodiments relate to an aerial robot that may include a distance sensor and visual inertial sensor. Embodiments also related to a method for the robot to perform height estimates using the distance sensor and the visual inertial sensor. The method may include determining a first height estimate of the aerial robot relative to a first region with a first surface level using data from a distance sensor of the aerial robot. The method may also include controlling the flight of the aerial robot over at least a part of the first region based on the first estimated height. The method may further include determining that the aerial robot is in a transition region between the first region and a second region with a second surface level different from the first surface level. The method may further include determining a second height estimate of the aerial robot using data from a visual inertial sensor of the aerial robot. The method may further include controlling the flight of the aerial robot using the second height estimate in the transition region. The aerial robot may include one or more processors and memory for storing instructions for performing the height estimate method. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram that illustrates a system environment of an example storage site, in accordance with some embodiments. 
         FIG.  2    is a block diagram that illustrates components of an example robot and an example base station, in accordance with some embodiments. 
         FIG.  3    is a flowchart that depicts an example process for managing the inventory of a storage site, in accordance with some embodiments. 
         FIG.  4    is a conceptual diagram of an example layout of a storage site that is equipped with a robot, in accordance with some embodiments. 
         FIG.  5    is a flowchart depicting an example navigation process of a robot, in accordance with some embodiments. 
         FIG.  6 A  is a conceptual diagram illustrating a flight path of an aerial robot. 
         FIG.  6 B  is a conceptual diagram illustrating a flight path of an aerial robot, in accordance with some embodiments. 
         FIG.  6 C  is a flowchart depicting an example process for estimating the vertical height level of an aerial robot, in accordance with some embodiments. 
         FIG.  7 A  is a block diagram illustrating an example height estimate algorithm, in accordance with some embodiments. 
         FIG.  7 B  is a conceptual diagram illustrating the use of different functions of a height estimate algorithm and sensor data as an aerial robot flies over an obstacle and maintains a level flight, in accordance with some embodiments. 
         FIG.  8    is a block diagram illustrating an example machine learning model, in accordance with some embodiments. 
         FIG.  9    is a block diagram illustrating components of an example computing machine, in accordance with some embodiments. 
     
    
    
     The figures depict, and the detailed description describes, various non-limiting embodiments for purposes of illustration only. 
     DETAILED DESCRIPTION 
     The figures (FIGs.) and the following description relate to preferred embodiments by way of illustration only. One of skill in the art may recognize alternative embodiments of the structures and methods disclosed herein as viable alternatives that may be employed without departing from the principles of what is disclosed. 
     Reference will now be made in detail to several embodiments, examples of which are illustrated in the accompanying figures. It is noted that wherever practicable similar or like reference numbers may be used in the figures and may indicate similar or like functionality. The figures depict embodiments of the disclosed system (or method) for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. 
     Embodiments relate to an aerial robot that navigates an environment with a level flight by accurately estimating the height of the robot using a combination of a distance sensor and a visual inertial sensor. The distance sensor and the visual inertial sensor may use different methods to estimate heights. Data generated from the two sensors may be used to compensate each other to provide an accurate height estimate. In some embodiments, the aerial robot may use the distance sensor to estimate the heights when the aerial robot travels over leveled surfaces. The aerial robot may also monitor the bias between the data from the two different sensors. At a transition region between two leveled surfaces, the aerial robot may switch to the visual inertial sensor. The aerial robot may adjust the data from the visual inertial sensor using the monitored biased. 
     System Overview 
       FIG.  1    is a block diagram that illustrates a system environment  100  of an example robotically-assisted or fully autonomous storage site, in accordance with some embodiments. By way of example, the system environment  100  includes a storage site  110 , a robot  120 , a base station  130 , an inventory management system  140 , a computing server  150 , a data store  160 , and a user device  170 . The entities and components in the system environment  100  communicate with each other through the network  180 . In various embodiments, the system environment  100  may include different, fewer, or additional components. Also, while each of the components in the system environment  100  is described in a singular form, the system environment  100  may include one or more of each of the components. For example, the storage site  110  may include one or more robots  120  and one or more base stations  130 . Each robot  120  may have a corresponding base station  130  or multiple robots  120  may share a base station  130 . 
     A storage site  110  may be any suitable facility that stores, sells, or displays inventories such as goods, merchandise, groceries, articles and collections. Example storage sites  110  may include warehouses, inventory sites, bookstores, shoe stores, outlets, other retail stores, libraries, museums, etc. A storage site  110  may include a number of regularly shaped structures. Regularly shaped structures may be structures, fixtures, equipment, furniture, frames, shells, racks, or other suitable things in the storage site  110  that have a regular shape or outline that can be readily identifiable, whether the things are permanent or temporary, fixed or movable, weight-bearing or not. The regularly shaped structures are often used in a storage site  110  for storage of inventory. For example, racks (including metallic racks, shells, frames, or other similar structures) are often used in a warehouse for the storage of goods and merchandise. However, not all regularly shaped structures may need to be used for inventory storage. A storage site  110  may include a certain layout that allows various items to be placed and stored systematically. For example, in a warehouse, the racks may be grouped by sections and separated by aisles. Each rack may include multiple pallet locations that can be identified using a row number and a column number. A storage site may include high racks and low racks, which may, in some case, largely carry most of the inventory items near the ground level. 
     A storage site  110  may include one or more robots  120  that are used to keep track of the inventory and to manage the inventory in the storage site  110 . For the ease of reference, the robot  120  may be referred to in a singular form, even though more than one robot  120  may be used. Also, in some embodiments, there can be more than one type of robot  120  in a storage site  110 . For example, some robots  120  may specialize in scanning inventory in the storage site  110 , while other robots  120  may specialize in moving items. A robot  120  may also be referred to as an autonomous robot, an inventory cycle-counting robot, an inventory survey robot, an inventory detection robot, or an inventory management robot. An inventory robot may be used to track inventory items, move inventory items, and carry out other inventory management tasks. The degree of autonomy may vary from embodiments to embodiments. For example, in some embodiments, the robot  120  may be fully autonomous so that the robot  120  automatically performs assigned tasks. In another embodiment, the robot  120  may be semi-autonomous such that it can navigate through the storage site  110  with minimal human commands or controls. In some embodiments, no matter what the degree of autonomy it has, a robot  120  may also be controlled remotely and may be switched to a manual mode. The robot  120  may take various forms such as an aerial drone, a ground robot, a vehicle, a forklift, and a mobile picking robot. 
     A base station  130  may be a device for the robot  120  to return and, for an aerial robot, to land. The base station  130  may include more than one return site. The base station  130  may be used to repower the robot  120 . Various ways to repower the robot  120  may be used in different embodiments. For example, in some embodiments, the base station  130  serves as a battery-swapping station that exchanges batteries on a robot  120  as the robot arrives at the base station to allow the robot  120  to quickly resume duty. The replaced batteries may be charged at the base station  130 , wired or wirelessly. In another embodiment, the base station  130  serves as a charging station that has one or more charging terminals to be coupled to the charging terminal of the robot  120  to recharge the batteries of the robot  120 . In yet another embodiment, the robot  120  may use fuel for power and the base station  130  may repower the robot  120  by filling its fuel tank. 
     The base station  130  may also serve as a communication station for the robot  120 . For example, for certain types of storage sites  110  such as warehouses, network coverage may not be present or may only be present at certain locations. The base station  130  may communicate with other components in the system environment  100  using wireless or wired communication channels such as Wi-Fi or an Ethernet cable. The robot  120  may communicate with the base station  130  when the robot  120  returns to the base station  130 . The base station  130  may send inputs such as commands to the robot  120  and download data captured by the robot  120 . In embodiments where multiple robots  120  are used, the base station  130  may be equipped with a swarm control unit or algorithm to coordinate the movements among the robots. The base station  130  and the robot  120  may communicate in any suitable ways such as radio frequency, Bluetooth, near-field communication (NFC), or wired communication. While, in some embodiments, the robot  120  mainly communicates to the base station, in other embodiments the robot  120  may also have the capability to directly communicate with other components in the system environment  100 . In some embodiments, the base station  130  may serve as a wireless signal amplifier for the robot  120  to directly communicate with the network  180 . 
     The inventory management system  140  may be a computing system that is operated by the administrator (e.g., a company that owns the inventory, a warehouse management administrator, a retailer selling the inventory) using the storage site  110 . The inventory management system  140  may be a system used to manage the inventory items. The inventory management system  140  may include a database that stores data regarding inventory items and the items&#39; associated information, such as quantities in the storage site  110 , metadata tags, asset type tags, barcode labels and location coordinates of the items. The inventory management system  140  may provide both front-end and back-end software for the administrator to access a central database and point of reference for the inventory and to analyze data, generate reports, forecast future demands, and manage the locations of the inventory items to ensure items are correctly placed. An administrator may rely on the item coordinate data in the inventory management system  140  to ensure that items are correctly placed in the storage site  110  so that the items can be readily retrieved from a storage location. This prevents an incorrectly placed item from occupying a space that is reserved for an incoming item and also reduces time to locate a missing item at an outbound process. 
     The computing server  150  may be a server that is tasked with analyzing data provided by the robot  120  and provide commands for the robot  120  to perform various inventory recognition and management tasks. The robot  120  may be controlled by the computing server  150 , the user device  170 , or the inventory management system  140 . For example, the computing server  150  may direct the robot  120  to scan and capture pictures of inventory stored at various locations at the storage site  110 . Based on the data provided by the inventory management system  140  and the ground truth data captured by the robot  120 , the computing server  150  may identify discrepancies in two sets of data and determine whether any items may be misplaced, lost, damaged, or otherwise should be flagged for various reasons. In turn, the computing server  150  may direct a robot  120  to remedy any potential issues such as moving a misplaced item to the correct position. In some embodiments, the computing server  150  may also generate a report of flagged items to allow site personnel to manually correct the issues. 
     The computing server  150  may include one or more computing devices that operate at different locations. For example, a part of the computing server  150  may be a local server that is located at the storage site  110 . The computing hardware such as the processor may be associated with a computer on site or may be included in the base station  130 . Another part of the computing server  150  may be a cloud server that is geographically distributed. The computing server  150  may serve as a ground control station (GCS), provide data processing, and maintain end-user software that may be used in a user device  170 . A GCS may be responsible for the control, monitor and maintenance of the robot  120 . In some embodiments, GCS is located on-site as part of the base station  130 . The data processing pipeline and end-user software server may be located remotely or on-site. 
     The computing server  150  may maintain software applications for users to manage the inventory, the base station  130 , and the robot  120 . The computing server  150  and the inventory management system  140  may or may not be operated by the same entity. In some embodiments, the computing server  150  may be operated by an entity separated from the administrator of the storage site. For example, the computing server  150  may be operated by a robotic service provider that supplies the robot  120  and related systems to modernize and automate a storage site  110 . The software application provided by the computing server  150  may take several forms. In some embodiments, the software application may be integrated with or as an add-on to the inventory management system  140 . In another embodiment, the software application may be a separate application that supplements or replaces the inventory management system  140 . In some embodiments, the software application may be provided as software as a service (SaaS) to the administrator of the storage site  110  by the robotic service provider that supplies the robot  120 . 
     The data store  160  includes one or more storage units such as memory that takes the form of non-transitory and non-volatile computer storage medium to store various data that may be uploaded by the robot  120  and inventory management system  140 . For example, the data stored in data store  160  may include pictures, sensor data, and other data captured by the robot  120 . The data may also include inventory data that is maintained by the inventory management system  140 . The computer-readable storage medium is a medium that does not include a transitory medium such as a propagating signal or a carrier wave. The data store  160  may take various forms. In some embodiments, the data store  160  communicates with other components by the network  180 . This type of data store  160  may be referred to as a cloud storage server. Example cloud storage service providers may include AWS, AZURE STORAGE, GOOGLE CLOUD STORAGE, etc. In another embodiment, instead of a cloud storage server, the data store  160  is a storage device that is controlled and connected to the computing server  150 . For example, the data store  160  may take the form of memory (e.g., hard drives, flash memories, discs, ROMs, etc.) used by the computing server  150  such as storage devices in a storage server room that is operated by the computing server  150 . 
     The user device  170  may be used by an administrator of the storage site  110  to provide commands to the robot  120  and to manage the inventory in the storage site  110 . For example, using the user device  170 , the administrator can provide task commands to the robot  120  for the robot to automatically complete the tasks. In one case, the administrator can specify a specific target location or a range of storage locations for the robot  120  to scan. The administrator may also specify a specific item for the robot  120  to locate or to confirm placement. Examples of user devices  170  include personal computers (PCs), desktop computers, laptop computers, tablet computers, smartphones, wearable electronic devices such as smartwatches, or any other suitable electronic devices. 
     The user device  170  may include a user interface  175 , which may take the form of a graphical user interface (GUI). Software application provided by the computing server  150  or the inventory management system  140  may be displayed as the user interface  175 . The user interface  175  may take different forms. In some embodiments, the user interface  175  is part of a front-end software application that includes a GUI displayed at the user device  170 . In one case, the front-end software application is a software application that can be downloaded and installed at user devices  170  via, for example, an application store (e.g., App Store) of the user device  170 . In another case, the user interface  175  takes the form of a Web interface of the computing server  150  or the inventory management system  140  that allows clients to perform actions through web browsers. In another embodiment, user interface  175  does not include graphical elements but communicates with the computing server  150  or the inventory management system  140  via other suitable ways such as command windows or application program interfaces (APIs). 
     The communications among the robot  120 , the base station  130 , the inventory management system  140 , the computing server  150 , the data store  160 , and the user device  170  may be transmitted via a network  180 , for example, via the Internet. In some embodiments, the network  180  uses standard communication technologies and/or protocols. Thus, the network  180  can include links using technologies such as Ethernet,  802 . 11 , worldwide interoperability for microwave access (WiMAX), 3G, 4G, LTE, 5G, digital subscriber line (DSL), asynchronous transfer mode (ATM), InfiniBand, PCI Express, etc. Similarly, the networking protocols used on the network  180  can include multiprotocol label switching (MPLS), the transmission control protocol/Internet protocol (TCP/IP), the user datagram protocol (UDP), the hypertext transport protocol (HTTP), the simple mail transfer protocol (SMTP), the file transfer protocol (FTP), etc. The data exchanged over the network  180  can be represented using technologies and/or formats including the hypertext markup language (HTML), the extensible markup language (XML), etc. In addition, all or some of the links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), virtual private networks (VPNs), Internet protocol security (IPsec), etc. The network  180  also includes links and packet switching networks such as the Internet. In some embodiments, two computing servers, such as computing server  150  and inventory management system  140 , may communicate through APIs. For example, the computing server  150  may retrieve inventory data from the inventory management system  140  via an API. 
     Example Robot and Base Station 
       FIG.  2    is a block diagram illustrating components of an example robot  120  and an example base station  130 , in accordance with some embodiments. The robot  120  may include an image sensor  210 , a processor  215 , memory  220 , a flight control unit (FCU)  225  that includes an inertial measurement unit (IMU)  230 , a state estimator  235 , a visual reference engine  240 , a planner  250 , a communication engine  255 , an I/O interface  260 , and a power source  265 . The functions of the robot  120  may be distributed among various components in a different manner than described below. In various embodiments, the robot  120  may include different, fewer, and/or additional components. Also, while each of the components in  FIG.  2    is described in a singular form, the components may present in plurality. For example, a robot  120  may include more than one image sensor  210  and more than one processor  215 . 
     The image sensor  210  captures images of an environment of a storage site for navigation, localization, collision avoidance, object recognition and identification, and inventory recognition purposes. A robot  120  may include more than one image sensors  210  and more than one type of such image sensors  210 . For example, the robot  120  may include a digital camera that captures optical images of the environment for the state estimator  235 . For example, data captured by the image sensor  210  may also be provided to the VIO unit  236  that may be included in the state estimator  235  for localization purposes such as to determine the position and orientation of the robot  120  with respect to an inertial frame, such as a global frame whose location is known and fixed. The robot  120  may also include a stereo camera that includes two or more lenses to allow the image sensor  210  to capture three-dimensional images through stereoscopic photography. For each image frame, the stereo camera may generate pixel values such as in red, green, and blue (RGB) and point cloud data that includes depth information. The images captured by the stereo camera may be provided to visual reference engine  240  for object recognition purposes. The image sensor  210  may also be another type of image sensor such as a light detection and ranging (LIDAR) sensor, an infrared camera, and 360-degree depth cameras. The image sensor  210  may also capture pictures of labels (e.g., barcodes) on items for inventory cycle-counting purposes. In some embodiments, a single stereo camera may be used for various purposes. For example, the stereo camera may provide image data to the visual reference engine  240  for object recognition. The stereo camera may also be used to capture pictures of labels (e.g., barcodes). In some embodiments, the robot  120  includes a rotational mount such as a gimbal that allows the image sensor  210  to rotate in different angles and to stabilize images captured by the image sensor  210 . In some embodiments, the image sensor  210  may also capture data along the path for the purpose of mapping the storage site. 
     The robot  120  includes one or more processors  215  and one or more memories  220  that store one or more sets of instructions. The one or more sets of instructions, when executed by one or more processors, cause the one or more processors to carry out processes that are implemented as one or more software engines. Various components, such as FCU  225  and state estimator  235 , of the robot  120  may be implemented as a combination of software and hardware (e.g., sensors). The robot  120  may use a single general processor to execute various software engines or may use separate more specialized processors for different functionalities. In some embodiments, the robot  120  may use a general-purpose computer (e.g., a CPU) that can execute various instruction sets for various components (e.g., FCU  225 , visual reference engine  240 , state estimator  235 , planner  250 ). The general-purpose computer may run on a suitable operating system such as LINUX, ANDROID, etc. For example, in some embodiments, the robot  120  may carry a smartphone that includes an application used to control the robot. In another embodiment, the robot  120  includes multiple processors that are specialized in different functionalities. For example, some of the functional components such as FCU  225 , visual reference engine  240 , state estimator  235 , and planner  250  may be modularized and each includes its own processor, memory, and a set of instructions. The robot  120  may include a central processor unit (CPU) to coordinate and communicate with each modularized component. Hence, depending on embodiments, a robot  120  may include a single processor or multiple processors  215  to carry out various operations. The memory  220  may also store images and videos captured by the image sensor  210 . The images may include images that capture the surrounding environment and images of the inventory such as barcodes and labels. 
     The flight control unit (FCU)  225  may be a combination of software and hardware, such as inertial measurement unit (IMU)  230  and other sensors, to control the movement of the robot  120 . For ground robot  120 , the flight control unit  225  may also be referred to as a microcontroller unit (MCU). The FCU  225  relies on information provided by other components to control the movement of the robot  120 . For example, the planner  250  determines the path of the robot  120  from a starting point to a destination and provides commands to the FCU  225 . Based on the commands, the FCU  225  generates electrical signals to various mechanical parts (e.g., actuators, motors, engines, wheels) of the robot  120  to adjust the movement of the robot  120 . The precise mechanical parts of the robots  120  may depend on the embodiments and the types of robots  120 . 
     The IMU  230  may be part of the FCU  225  or may be an independent component. The IMU  230  may include one or more accelerometers, gyroscopes, and other suitable sensors to generate measurements of forces, linear accelerations, and rotations of the robot  120 . For example, the accelerometers measure the force exerted on the robot  120  and detect the linear acceleration. Multiple accelerometers cooperate to detect the acceleration of the robot  120  in the three-dimensional space. For instance, a first accelerometer detects the acceleration in the x-direction, a second accelerometer detects the acceleration in the y-direction, and a third accelerometer detects the acceleration in the z-direction. The gyroscopes detect the rotations and angular acceleration of the robot  120 . Based on the measurements, a processor  215  may obtain the estimated localization of the robot  120  by integrating the translation and rotation data of the IMU  230  with respect to time. The IMU  230  may also measure the orientation of the robot  120 . For example, the gyroscopes in the IMU  230  may provide readings of the pitch angle, the roll angle, and the yaw angle of the robot  120 . 
     The state estimator  235  may correspond to a set of software instructions stored in the memory  220  that can be executed by the processor  215 . The state estimator  235  may be used to generate localization information of the robot  120  and may include various sub-components for estimating the state of the robot  120 . For example, in some embodiments, the state estimator  235  may include a visual-inertial odometry (VIO) unit  236  and an height estimator  238 . In other embodiments, other modules, sensors, and algorithms may also be used in the state estimator  235  to determine the location of the robot  120 . 
     The VIO unit  236  receives image data from the image sensor  210  (e.g., a stereo camera) and measurements from IMU  230  to generate localization information such as the position and orientation of the robot  120 . The localization data obtained from the double integration of the acceleration measurements from the IMU  230  is often prone to drift errors. The VIO unit  236  may extract image feature points and tracks the feature points in the image sequence to generate optical flow vectors that represent the movement of edges, boundaries, surfaces of objects in the environment captured by the image sensor  210 . Various signal processing techniques such as filtering (e.g., Wiener filter, Kalman filter, bandpass filter, particle filter) and optimization, and data/image transformation may be used to reduce various errors in determining localization information. The localization data generated by the VIO unit  236  may include an estimate of the pose of the robot  120 , which may be expressed in terms of the roll angle, the pitch angle, and the yaw angle of the robot  120 . 
     The height estimator  238  may be a combination of software and hardware that are used to determine the absolute height and relative height (e.g., distance from an object that lies on the floor) of the robot  120 . The height estimator  238  may include a downward distance sensor  239  that may measure the height relative to the ground or to an object underneath the robot  120 . The distance sensor  239  may be electromagnetic wave based, laser based, optics based, sonar based, ultrasonic based, or another suitable signal based. For example, the distance sensor  239  may be a laser range finder, a lidar range finder, a sonar range finder, an ultrasonic range finder, or a radar. A range finder may include one or more emitters that emit signals (e.g., infrared, laser, sonar, etc.) and one or more sensors that detect the round trip time of the signal reflected by an object. In some embodiments, the robot  120  may be equipped with a single emitter range finder. The height estimator  238  may also receive data from the VIO unit  236  that may estimate the height of the robot  120 , but usually in a less accurate fashion compared to a distance sensor  239 . The height estimator  238  may include software algorithms to combine data generated by the distance sensor  239  and the data generated by the VIO unit  236  as the robot  120  flies over various objects and inventory that are placed on the floor or other horizontal levels. The data generated by the height estimator  238  may be used for collision avoidance and finding a target location. The height estimator  238  may set a global maximum altitude to prevent the robot  120  from hitting the ceiling. The height estimator  238  also provides information regarding how many rows in the rack are below the robot  120  for the robot  120  to locate a target location. The height data may be used in conjunction with the count of rows that the robot  120  has passed to determine the vertical level of the robot  120 . The height estimation will be discussed in further detail with reference to  FIG.  6 A  through  FIG.  7 B . 
     The visual reference engine  240  may correspond to a set of software instructions stored in the memory  220  that can be executed by the processor  215 . The visual reference engine  240  may include various image processing algorithm and location algorithm to determine the current location of the robot  120 , to identify the objects, edges, and surfaces of the environment near the robot  120 , and to determine an estimated distance and orientation (e.g., yaw) of the robot  120  relative to a nearby surface of an object. The visual reference engine  240  may receive pixel data of a series of images and point cloud data from the image sensor  210 . The location information generated by the visual reference engine  240  may include distance and yaw from an object and center offset from a target point (e.g., a midpoint of a target object). 
     The visual reference engine  240  may include one or more algorithms and machine learning models to create image segmentations from the images captured by the image sensor  210 . The image segmentation may include one or more segments that separate the frames (e.g., vertical or horizontal bars of racks) or outlines of regularly shaped structures appearing in the captured images from other objects and environments. The algorithms used for image segmentation may include a convolutional neural network (CNN). In performing the segmentation, other image segmentation algorithms such as edge detection algorithms (e.g., Canny operator, Laplacian operator, Sobel operator, Prewitt operator), corner detection algorithms, Hough transform, and other suitable feature detection algorithms may also be used. 
     The visual reference engine  240  also performs object recognition (e.g., object detection and further analyses) and keeps track of the relative movements of the objects across a series of images. The visual reference engine  240  may track the number of regularly shaped structures in the storage site  110  that are passed by the robot  120 . For example, the visual reference engine  240  may identify a reference point (e.g., centroid) of a frame of a rack and determine if the reference point passes a certain location of the images across a series of images (e.g., whether the reference point passes the center of the images). If so, the visual reference engine  240  increments the number of regularly shaped structures that have been passed by the robot  120 . 
     The robot  120  may use various components to generate various types of location information (including location information relative to nearby objects and localization information). For example, in some embodiments, the state estimator  235  may process the data from the VIO unit  236  and the height estimator  238  to provide localization information to the planner  250 . The visual reference engine  240  may count the number of regularly shaped structures that the robot  120  has passed to determine a current location. The visual reference engine  240  may generate location information relative to nearby objects. For example, when the robot  120  reaches a target location of a rack, the visual reference engine  240  may use point cloud data to reconstruct a surface of the rack and use the depth data from the point cloud to determine more accurate yaw and distance between the robot  120  and the rack. The visual reference engine  240  may determine a center offset, which may correspond to the distance between the robot  120  and the center of a target location (e.g., the midpoint of a target location of a rack). Using the center offset information, the planner  250  controls the robot  120  to move to the target location and take a picture of the inventory in the target location. When the robot  120  changes direction (e.g., rotations, transitions from horizontal movement to vertical movement, transitions from vertical movement to horizontal movement, etc.), the center offset information may be used to determine the accurate location of the robot  120  relative to an object. 
     The planner  250  may correspond to a set of software instructions stored in the memory  220  that can be executed by the processor  215 . The planner  250  may include various routing algorithms to plan a path of the robot  120  as the robot travels from a first location (e.g., a starting location, the current location of the robot  120  after finishing the previous journey) to a second location (e.g., a target destination). The robot  120  may receive inputs such as user commands to perform certain actions (e.g., scanning of inventory, moving an item, etc.) at certain locations. The planner  250  may include two types of routes, which corresponds to a spot check and a range scan. In a spot check, the planner  250  may receive an input that includes coordinates of one or more specific target locations. In response, the planner  250  plans a path for the robot  120  to travel to the target locations to perform an action. In a range scan, the input may include a range of coordinates corresponding to a range of target locations. In response, the planner  250  plans a path for the robot  120  to perform a full scan or actions for the range of target locations. 
     The planner  250  may plan the route of the robot  120  based on data provided by the visual reference engine  240  and the data provided by the state estimator  235 . For example, the visual reference engine  240  estimates the current location of the robot  120  by tracking the number of regularly shaped structures in the storage site  110  passed by the robot  120 . Based on the location information provided by the visual reference engine  240 , the planner  250  determines the route of the robot  120  and may adjust the movement of the robot  120  as the robot  120  travels along the route. 
     The planner  250  may also include a fail-safe mechanism in the case where the movement of the robot  120  has deviated from the plan. For example, if the planner  250  determines that the robot  120  has passed a target aisle and traveled too far away from the target aisle, the planner  250  may send signals to the FCU  225  to try to remedy the path. If the error is not remedied after a timeout or within a reasonable distance, or the planner  250  is unable to correctly determine the current location, the planner  250  may direct the FCU to land or to stop the robot  120 . 
     Relying on various location information, the planner  250  may also include algorithms for collision avoidance purposes. In some embodiments, the planner  250  relies on the distance information, the yaw angle, and center offset information relative to nearby objects to plan the movement of the robot  120  to provide sufficient clearance between the robot  120  and nearby objects. Alternatively, or additionally, the robot  120  may include one or more depth cameras such as a 360-degree depth camera set that generates distance data between the robot  120  and nearby objects. The planner  250  uses the location information from the depth cameras to perform collision avoidance. 
     The communication engine  255  and the I/O interface  260  are communication components to allow the robot  120  to communicate with other components in the system environment  100 . A robot  120  may use different communication protocols, wireless or wired, to communicate with an external component such as the base station  130 . Example communication protocols may include Wi-Fi, Bluetooth, NFC, USB, etc. that couple the robot  120  to the base station  130 . The robot  120  may transmit various types of data, such as image data, flight logs, location data, inventory data, and robot status information. The robot  120  may also receive inputs from an external source to specify the actions that need to be performed by the robot  120 . The commands may be automatically generated or manually generated by an administrator. The communication engine  255  may include algorithms for various communication protocols and standards, encoding, decoding, multiplexing, traffic control, data encryption, etc. for various communication processes. The I/O interface  260  may include software and hardware component such as hardware interface, antenna, and so forth for communication. 
     The robot  120  also includes a power source  265  used to power various components and the movement of the robot  120 . The power source  265  may be one or more batteries or a fuel tank. Example batteries may include lithium-ion batteries, lithium polymer (LiPo) batteries, fuel cells, and other suitable battery types. The batteries may be placed inside permanently or may be easily replaced. For example, batteries may be detachable so that the batteries may be swapped when the robot  120  returns to the base station  130 . 
     While  FIG.  2    illustrates various example components, a robot  120  may include additional components. For example, some mechanical features and components of the robot  120  are not shown in  FIG.  2   . Depending on its type, the robot  120  may include various types of motors, actuators, robotic arms, lifts, other movable components, other sensors for performing various tasks. 
     Continuing to refer to  FIG.  2   , an example base station  130  includes a processor  270 , a memory  275 , an I/O interface  280 , and a repowering unit  285 . In various embodiments, the base station  130  may include different, fewer, and/or additional components. 
     The base station  130  includes one or more processors  270  and one or more memories  275  that include one or more set of instructions for causing the processors  270  to carry out various processes that are implemented as one or more software modules. The base station  130  may provide inputs and commands to the robot  120  for performing various inventory management tasks. The base station  130  may also include an instruction set for performing swarm control among multiple robots  120 . Swarm control may include task allocation, routing and planning, coordination of movements among the robots to avoid collisions, etc. The base station  130  may serve as a central control unit to coordinate the robots  120 . The memory  275  may also include various sets of instructions for performing analysis of data and images downloaded from a robot  120 . The base station  130  may provide various degrees of data processing from raw data format conversion to a full data processing that generates useful information for inventory management. Alternatively, or additionally, the base station  130  may directly upload the data downloaded from the robot  120  to a data store, such as the data store  160 . The base station  130  may also provide operation, administration, and management commands to the robot  120 . In some embodiments, the base station  130  can be controlled remotely by the user device  170 , the computing server  150 , or the inventory management system  140 . 
     The base station  130  may also include various types of I/O interfaces  280  for communications with the robot  120  and to the Internet. The base station  130  may communicate with the robot  120  continuously using a wireless protocol such as Wi-Fi or Bluetooth. In some embodiments, one or more components of the robot  120  in  FIG.  2    may be located in the base station and the base station may provide commands to the robot  120  for movement and navigation. Alternatively, or additionally, the base station  130  may also communicate with the robot  120  via short-range communication protocols such as NFC or wired connections when the robot  120  lands or stops at the base station  130 . The base station  130  may be connected to the network  180  such as the Internet. The wireless network (e.g., LAN) in some storage sites  110  may not have sufficient coverage. The base station  130  may be connected to the network  180  via an Ethernet cable. 
     The repowering unit  285  includes components that are used to detect the power level of the robot  120  and to repower the robot  120 . Repowering may be done by swapping the batteries, recharging the batteries, re-filling the fuel tank, etc. In some embodiments, the base station  130  includes mechanical actuators such as robotic arms to swap the batteries on the robot  120 . In another embodiment, the base station  130  may serve as the charging station for the robot  120  through wired charging or inductive charging. For example, the base station  130  may include a landing or resting pad that has an inductive coil underneath for wirelessly charging the robot  120  through the inductive coil in the robot. Other suitable ways to repower the robot  120  is also possible. 
     Example Inventory Management Process 
       FIG.  3    is a flowchart that depicts an example process for managing the inventory of a storage site, in accordance with some embodiments. The process may be implemented by a computer, which may be a single operation unit in a conventional sense (e.g., a single personal computer) or may be a set of distributed computing devices that cooperate to execute a set of instructions (e.g., a virtual machine, a distributed computing system, cloud computing, etc.). Also, while the computer is described in a singular form, the computer that performs the process in  FIG.  3    may include more than one computer that is associated with the computing server  150 , the inventory management system  140 , the robot  120 , the base station  130 , or the user device  170 . 
     In accordance with some embodiments, the computer receives  310  a configuration of a storage site  110 . The storage site  110  may be a warehouse, a retail store, or another suitable site. The configuration information of the storage site  110  may be uploaded to the robot  120  for the robot to navigate through the storage site  110 . The configuration information may include a total number of the regularly shaped structures in the storage site  110  and dimension information of the regularly shaped structures. The configuration information provided may take the form of a computer-aided design (CAD) drawing or another type of file format. The configuration may include the layout of the storage site  110 , such as the rack layout and placement of other regularly shaped structures. The layout may be a  2 -dimensional layout. The computer extracts the number of sections, aisles, and racks and the number of rows and columns for each rack from the CAD drawing by counting those numbers as appeared in the CAD drawing. The computer may also extract the height and the width of the cells of the racks from the CAD drawing or from another source. In some embodiments, the computer does not need to extract the accurate distances between a given pair of racks, the width of each aisle, or the total length of the racks. Instead, the robot  120  may measure dimensions of aisles, racks, and cells from a depth sensor data or may use a counting method performed by the planner  250  in conjunction with the visual reference engine  240  to navigate through the storage site  110  by counting the number of rows and columns the robot  120  has passed. Hence, in some embodiments, the accurate dimensions of the racks may not be needed. 
     Some configuration information may also be manually inputted by an administrator of the storage site  110 . For example, the administrator may provide the number of sections, the number of aisles and racks in each section, and the size of the cells of the racks. The administrator may also input the number of rows and columns of each rack. 
     Alternatively, or additionally, the configuration information may also be obtained through a mapping process such as a pre-flight mapping or a mapping process that is conducted as the robot  120  carries out an inventory management task. For example, for a storage site  110  that newly implements the automated management process, an administrator may provide the size of the navigable space of the storage site for one or more mapping robots to count the numbers of sections, aisles, rows and columns of the regularly shaped structures in the storage site  110 . Again, in some embodiments, the mapping or the configuration information does not need to measure the accurate distance among racks or other structures in the storage site  110 . Instead, a robot  120  may navigate through the storage site  110  with only a rough layout of the storage site  110  by counting the regularly shaped structures along the path in order to identify a target location. The robotic system may gradually perform mapping or estimation of scales of various structures and locations as the robot  120  continues to perform various inventory management tasks. 
     The computer receives  320  inventory management data for inventory management operations at the storage site  110 . Certain inventory management data may be manually inputted by an administrator while other data may be downloaded from the inventory management system  140 . The inventory management data may include scheduling and planning for inventory management operations, including the frequency of the operations, time window, etc. For example, the management data may specify that each location of the racks in the storage site  110  is to be scanned every predetermined period (e.g., every day) and the inventory scanning process is to be performed in the evening by the robot  120  after the storage site is closed. The data in the inventory management system  140  may provide the barcodes and labels of items, the correct coordinates of the inventory, information regarding racks and other storage spaces that need to be vacant for incoming inventory, etc. The inventory management data may also include items that need to be retrieved from the storage site  110  (e.g., items on purchase orders that need to be shipped) for each day so that the robot  120  may need to focus on those items. 
     The computer generates  330  a plan for performing inventory management. For example, the computer may generate an automatic plan that includes various commands to direct the robot  120  to perform various scans. The commands may specify a range of locations that the robot  120  needs to scan or one or more specific locations that the robot  120  needs to go. The computer may estimate the time for each scanning trip and design the plan for each operation interval based on the available time for the robotic inventory management. For example, in certain storage sites  110 , robotic inventory management is not performed during the business hours. 
     The computer generates  340  various commands to operate one or more robots  120  to navigate the storage site  110  according to the plan and the information derived from the configuration of the storage site  110 . The robot  120  may navigate the storage site  110  by at least visually recognizing the regularly shaped structures in the storage sites and counting the number of regularly shaped structures. In some embodiments, in addition to the localization techniques such as VIO used, the robot  120  counts the number of racks, the number of rows, and the number of columns that it has passed to determine its current location along a path from a starting location to a target location without knowing the accurate distance and direction that it has traveled. 
     The scanning of inventory or other inventory management tasks may be performed autonomously by the robot  120 . In some embodiments, a scanning task begins at a base station at which the robot  120  receives  342  an input that includes coordinates of target locations in the storage site  110  or a range of target locations. The robot  120  departs  344  from the base station  130 . The robot  120  navigates  346  through the storage site  110  by visually recognizing regularly shaped structures. For example, the robot  120  tracks the number of regularly shaped structures that are passed by the robot  120 . The robot  120  makes turns and translation movements based on the recognized regularly shaped structures captured by the robot&#39;s image sensor  210 . Upon reaching the target location, the robot  120  may align itself with a reference point (e.g., the center location) of the target location. At the target location, the robot  120  captures  348  data (e.g., measurements, pictures, etc.) of the target location that may include the inventory item, barcodes, and labels on the boxes of the inventory item. If the initial command before the departure of the robot  120  includes multiple target locations or a range of target locations, the robot  120  continues to the next target locations by moving up, down, or sideways to the next location to continue to scanning operation. 
     Upon completion of a scanning trip, the robot  120  returns  350  to the base station  130  by counting the number of regularly shaped structures that the robot  120  has passed, in a reversed direction. The robot  120  may potentially recognize the structures that the robot has passed when the robot  120  travels to the target location. Alternatively, the robot  120  may also return to the base station  130  by reversing the path without any count. The base station  130  repowers the robot  120 . For example, the base station  130  provides the next commands for the robot  120  and swaps  352  the battery of the robot  120  so that the robot  120  can quickly return to service for another scanning trip. The used batteries may be charged at the base station  130 . The base station  130  also may download the data and images captured by the robot  120  and upload the data and images to the data store  160  for further process. Alternatively, the robot  120  may include a wireless communication component to send its data and images to the base station  130  or directly to the network  180 . 
     The computer performs  360  analyses of the data and images captured by the robot  120 . For example, the computer may compare the barcodes (including serial numbers) in the images captured by the robot  120  to the data stored in the inventory management system  140  to identify if any items are misplaced or missing in the storage site  110 . The computer may also determine other conditions of the inventory. The computer may generate a report to display at the user interface  175  for the administrator to take remedial actions for misplaced or missing inventory. For example, the report may be generated daily for the personnel in the storage site  110  to manually locate and move the misplaced items. Alternatively, or additionally, the computer may generate an automated plan for the robot  120  to move the misplaced inventory. The data and images captured by the robot  120  may also be used to confirm the removal or arrival of inventory items. 
     Example Navigation Process 
       FIG.  4    is a conceptual diagram of an example layout of a storage site  110  that is equipped with a robot  120 , in accordance with some embodiments.  FIG.  4    shows a two-dimensional layout of storage site  110  with an enlarged view of an example rack that is shown in inset  405 . The storage site  110  may be divided into different regions based on the regularly shaped structures. In this example, the regularly shaped structures are racks  410 . The storage site  110  may be divided by sections  415 , aisles  420 , rows  430  and columns  440 . For example, a section  415  is a group of racks. Each aisle may have two sides of racks. Each rack  410  may include one or more columns  440  and multiple rows  430 . The storage unit of a rack  410  may be referred to as a cell  450 . Each cell  450  may carry one or more pallets  460 . In this particular example, two pallets  460  are placed on each cell  450 . Inventory of the storage site  110  is carried on the pallets  460 . The divisions and nomenclature illustrated in  FIG.  4    are used as examples only. A storage site  110  in another embodiment may be divided in a different manner. Each inventory item in the storage site  110  may be located on a pallet  460 . The target location (e.g., a pallet location) of the inventory item may be identified using a coordinate system. For example, an item placed on a pallet  460  may have an aisle number (A), a rack number (K), a row number (R), and a column number (C). For example, a pallet location coordinate of [A 3 , K 1 , R 4 , and C 5 ] means that the pallet  460  is located at a rack  410  in the third aisle and the north rack. The location of the pallet  460  in the rack  410  is in the fourth row (counting from the ground) and the fifth column. In some cases, such as the particular layout shown in  FIG.  4   , an aisle  420  may include racks  410  on both sides. Additional coordinate information may be used to distinguish the racks  410  at the north side and the racks  410  at the south side of an aisle  420 . Alternatively, the top and bottom sides of the racks can have different aisle numbers. For a spot check, a robot  120  may be provided with a single coordinate if only one spot is provided or multiple coordinates if more than one spot is provided. For a range scan that checks a range of pallets  460 , the robot  120  may be provided with a range of coordinates, such as an aisle number, a rack number, a starting row, a starting column, an ending row, and an ending column. In some embodiments, the coordinate of a pallet location may also be referred in a different manner. For example, in one case, the coordinate system may take the form of “aisle-rack-shelf-position.” The shelf number may correspond to the row number and the position number may correspond to the column number. 
     Referring to  FIG.  5    in conjunction with  FIG.  4   ,  FIG.  5    is a flowchart depicting an example navigation process of a robot  120 , in accordance with some embodiments. The robot  120  receives  510  a target location  474  of a storage site  110 . The target location  474  may be expressed in the coordinate system as discussed above in association with  FIG.  4   . The target location  474  may be received as an input command from a base station  130 . The input command may also include the action that the robot  120  needs to take, such as taking a picture at the target location  474  to capture the barcodes and labels of inventory items. The robot  120  may rely on the VIO unit  236  and the height estimator  238  to generate localization information. In one case, the starting location of a route is the base station  130 . In some cases, the starting location of a route may be any location at the storage site  110 . For example, the robot  120  may have recently completed a task and started another task without returning to the base station  130 . 
     The processors of the robot  120 , such as the one executing the planner  250 , control  520  the robot  120  to the target location  474  along a path  470 . The path  470  may be determined based on the coordinate of the target location  474 . The robot  120  may turn so that the image sensor  210  is facing the regularly shaped structures (e.g., the racks). The movement of the robot  120  to the target location  474  may include traveling to a certain aisle, taking a turn to enter the aisle, traveling horizontally to the target column, traveling vertically to the target row, and turning to the right angle facing the target location  474  to capture a picture of inventory items on the pallet  460 . 
     As the robot  120  moves to the target location  474 , the robot  120  captures  530  images of the storage site  110  using the image sensor  210 . The images captured may be in a sequence of images. The robot  120  receives the images captured by the image sensor  210  as the robot  120  moves along the path  470 . The images may capture the objects in the environment, including the regularly shaped structures such as the racks. For example, the robot  120  may use the algorithms in the visual reference engine  240  to visually recognize the regularly shaped structures. 
     The robot  120  analyzes  540  the images captured by the image sensor  210  to determine the current location of the robot  120  in the path  470  by tracking the number of regularly shaped structures in the storage site passed by the robot  120 . The robot  120  may use various image processing and object recognition techniques to identify the regularly shaped structures and to track the number of structures that the robot  120  has passed. Referring to the path  470  shown in  FIG.  4   , the robot  120 , facing the racks  410 , may travel to the turning point  476 . The robot  120  determines that it has passed two racks  410  so it has arrived at the target aisle. In response, the robot  120  turns counter-clockwise and enter the target aisle facing the target rack. The robot  120  counts the number of columns that it has passed until the robot  120  arrives at the target column. Depending on the target row, the robot  120  may travel vertically up or down to reach the target location. Upon reaching the target location, the robot  120  performs the action specified by the input command, such as taking a picture of the inventory at the target location. 
     Example Level Flight Operations 
       FIG.  6 A  is a conceptual diagram illustrating a flight path of an aerial robot  602 . The aerial robot  602  travels over a first region  604  with a first surface level  605 , a second region  606  with a second surface level  607 , and a third region  608  with a third surface level  609 . For example, the first region  604  may correspond to the floor and the second and third regions  606  and  608  may correspond to obstacles on the floor (e.g., objects on the floor, or pallets and inventory items placed on the floor in the setting of a storage site).  FIG.  6 A  illustrates the challenge of navigating an aerial robot to perform a level flight with approximately constant heights, especially in settings that need to have accurate measurements of heights, such as for indoor flights or low altitude outdoor flights. Conventionally, an aerial robot may rely on a barometer to measure the pressure change in order to deduce its altitude. However, in an indoor or a low altitude setting, the pressure change may not be sufficiently significant or may even be unmeasurable to allow the aerial robot  602  to measure the height. 
       FIG.  6 A  illustrates the aerial robot  602  using a distance sensor to measure its height. The aerial robot  602  is programmed to maintain a constant distance from the surface over which the aerial robot  602  travels. While the distance sensor may produce relatively accurate distance measurements between the aerial robot  602  and the underneath surface, the distance sensor is unable to determine any change of levels of different regions because the distance sensor often measures the round trip time of a signal (e.g., laser) traveled from the sensor&#39;s emitter and reflected by a surface back to sensor&#39;s receiver. Since the second region  606  is elevated form the first region  604  and the third region  608  is further elevated, the aerial robot  602 , in maintaining a constant distance from the underlying surfaces, may show a flight path illustrated in  FIG.  6 A  and is unable to perform a level flight. 
     The failure to maintain a level flight could bring various challenges to the navigation of the aerial robot  602 . For example, the type of unwanted change in height shown in  FIG.  6 A  during a flight may affect the generation of location and localization data of the aerial robot  602  because of the drifts created in the change in height. In an indoor setting, an undetected increase in height may cause the aerial robot  602  to hit the ceiling of a building. In a setting of a storage site  110 , the flight path illustrated in  FIG.  6 A  may prevent the aerial robot  602  from performing a scan of inventory items or traveling across the same row of a storage rack. 
       FIG.  6 B  is a conceptual diagram illustrating a flight path of an aerial robot  610 , in accordance with some embodiments. The aerial robot  610  may be an example of the robot  120  as discussed in  FIG.  1    through  FIG.  5   . While the discussion in  FIG.  1    through  FIG.  5    focuses on the navigation of the robot  120  at a storage site, the height estimation discussed in  FIG.  6 B  through  FIG.  7 B  is not limited to an indoor setting. In addition to serving as the robot  120 , the aerial robot  610  may also be used in an outdoor setting such as in a low altitude flight that needs an accurate height measurement. In some embodiments, the height estimation process described in this disclosure may also be used with high altitude aerial robot in conjunction with or in place of a barometer. The aerial robot  610  may be a drone, an unmanned vehicle, an autonomous vehicle, or another suitable machine that is capable of flying. 
     In some embodiments, the aerial robot  610  is equipped with a distance sensor (e.g., the distance sensor  239 ) and a visual inertial sensor (e.g., the VIO unit  236 ). The aerial robot  610  may rely on the fusion of analyses of the distance sensor and visual inertial sensor to navigate the aerial robot  610  to maintain a level flight, despite the change in the surface levels in regions  604 ,  606 , and  608 . Again, the first region  604  may correspond to the floor and the second and third regions  606  and  608  may correspond to obstacles on the floor (e.g., objects on the floor, or pallets and inventory items placed on the floor in the setting of a storage site). 
     The aerial robot  610  may use data from both sensors to compensate for and adjust data of each other for determining a vertical height estimate regardless of whether the aerial robot  610  is traveling over the first region  604 , the second region  606 , or the third region  608 . A distance sensor may return highly accurate measurements (with errors within feet, sometimes inches, or even smaller errors) of distance readings based on the round-trip time of the signal transmitted from the distance sensor&#39;s transmitter and reflected by a nearby surface at which the transmitter is pointing. However, the distance readings from the distance sensor may be affected by nearby environment changes such as the presence of an obstacle that elevates the surface at which the distance sensor&#39;s transmitter is pointing. Also, the orientation of the distance sensor may also not be directly pointing downward due to the orientation of the aerial robot  610 . For example, in  FIG.  6 B , the aerial robot  610  is illustrated as having a negative pitch angle  620  and a positive roll angle  622 . As a result, the signal emitted by the distance sensor travels along a path  624 , which is not a completely vertical path. The aerial robot  610  determines its pitch angle  620  and the roll angle  622  using an IMU (such as IMU  230 ). The data of the pitch angle  620  and the roll angle  622  may be a part of the VIO data provided by the visual inertial sensor or may be an independent data provided directly by the IMU. Using the pitch angle  620  and the roll angle  622 , the aerial robot  610  may determine the first height estimate  630  based on the reading of the distance sensor. The flight of the aerial robot  610  over at least a part of the first region  604  may be controlled based on the first estimated height. However, when the aerial robot  610  travels over the second region  606 , the distance readings from the distance sensor will suddenly decrease due to the elevation in the second region  606 . 
     A visual inertial sensor (e.g., the VIO unit  236 ), or simply an inertial sensor, may be less susceptible to environmental changes such as the presence of obstacles in the second and third regions  606  and  608 . An inertial sensor may also simply be an inertial sensor such as the IMU  230  or include the visual element such as the VIO unit  236 . An inertial sensor provides localization data of the aerial robot  610  based on the accelerometers and gyroscopes in an IMU. Since the IMU is internal to the aerial robot  610 , the localization data is not measured relative to a nearby object or surface. Thus, the data is usually also not affected by a nearby object or surface. However, the position data (including a vertical height estimate) generated from an inertial sensor is often obtained by twice integrating, with respect to time, the acceleration data obtained from the accelerometers of an IMU. The localization data is prone to drift and could become less accurate as the aerial robot  610  travels a relatively long distance. 
     The aerial robot  610  may use data from a visual inertial sensor to compensate the data generated by the distance sensor in regions of transitions that are associated with a change in surface levels. In some embodiments, in regions of transitions, such as regions  640 ,  642 ,  644 , and  646 , the data from the distance sensor may become unstable due to sudden changes in the surface levels. The aerial robot  610  may temporarily switch to the visual inertial senor to estimate its vertical height. After the transition regions, the aerial robot  610  may revert to the distance sensor. Relying on both types of sensor data, the aerial robot  610  may travel in a relatively level manner (relatively at the same horizontal level), as illustrated in  FIG.  6 B . The details of the height estimate process and the determination of the transition regions will be further discussed with reference to  FIG.  6 C  through  FIG.  7 B . 
     Example Height Estimation Process 
       FIG.  6 C  is a flowchart depicting an example process for estimating the vertical height level of an aerial robot  610  as the aerial robot  610  travel over different regions that have various surface levels, in accordance with some embodiments. The aerial robot  610  may be equipped with a distance sensor and a visual inertial sensor. The aerial robot  610  may also include one or more processors and memory for storing code instructions. The instructions, when executed by the one or more processors, may cause the one or more processors to perform the process described in  FIG.  6 C . The one or more processors may correspond to the processor  215  and a processor in the FCU  225 . For simplicity, the one or more processors may be referred to as “a processor” or “the processor” below, even though each step in the process described in  FIG.  6 C  may be performed by the same processor or different processors of the aerial robot  610 . Also, the process illustrated in  FIG.  6 C  is discussed in conjunction with the visual illustration in  FIG.  6 B . 
     In some embodiments, the aerial robot  610  may determine  650  a first height estimate  630  of the aerial robot  610  relative to a first region  604  with a first surface level  605  using data from the distance sensor. For example, the data from the distance sensor may take the form of a time series of distance readings from the distance sensor. For a particular instance, a processor of the aerial robot  610  may receive a distance reading from the data of the distance sensor. The processor may also receive a pose of the aerial robot  610 . The pose may include a pitch angle  620 , a roll angle  622 , and a yaw angle. In some embodiments, the aerial robot  610  may use one or more angles related to the pose to determine the first height estimate  630  from the distance reading adjusted by the pitch angle  620  the roll angle  622 . For example, the processor may use one or more trigonometry relationship to convert the distance reading to the first height estimate  630 . 
     The processor controls  655  the flight of the aerial robot  610  over at least a part of the first region based on the first estimated height  630 . As the aerial robot  610  travels over the first region  604 , the readings from the distance sensor should be relatively stable. The aerial robot  610  may also monitor the data of the visual inertial sensor. The data of the visual inertial sensor may also be a time series of readings of localization data that include readings of height estimates. The readings of distance data from the distance sensor may be generated by, for example, a laser range finder while the readings of location data in the z-direction from the visual inertial sensor may be generated by double integrating the z-direction accelerometer&#39;s data with respect to time. Since the two sensors estimate the height using different sources and methods, the readings from the two sensors may not agree. In addition, the readings from the visual inertial sensor may also be affected by drifts. The aerial robot  610  may monitor the readings from the visual inertial sensors and determine a bias between the readings form the visual inertial sensor and the readings from the distance sensor. The bias may be the difference between the two readings. 
     The processor determines  660  that the aerial robot  610  is in a transition region  640  between the first region  604  and a second region  606  with a second surface level  607  that is different from the first surface level  605 . A transition region may be a region where the surface levels are changing. The transition region may indicate the presence of an obstacle on the ground level, such as an object that prevents the distance sensor&#39;s signal from reaching the ground. For example, in the setting of a storage site, the transition region may be at the boundary of a pallet or an inventory item placed on the floor. 
     In various embodiments, a transition region and its size may be defined differently, depending on the implementation of the height estimation algorithm. In some embodiments, the transition region may be defined based on a predetermined length in the horizontal direction. For example, the transition region may be a fixed length after the distance sensor detects a sudden change in distance readings. In another embodiment, the transition region may be defined based on a duration of time. For example, the transition region may be a time duration after the distance sensor detects a sudden change in distance readings. The time may be a predetermined period or a relative period determined based on the speed of the aerial robot  610  in the horizontal direction. 
     In yet another embodiment, the transition region may be defined as a region in which the processor becomes uncertain that the aerial robot  610  is in a leveled region. For example, the aerial robot  610  may include, in its memory, one or more probabilistic models that determine the likelihood that the aerial robot  610  is traveling in a leveled region. The likelihood may be determined based on the readings of the distance data from the distance sensor, which should be relatively stable when the aerial robot  610  is traveling over a leveled region. If the likelihood that the aerial robot  610  is traveling in a leveled region is below a threshold value, the processor may determine that the aerial robot  610  is in a transition region. For example, in some embodiments, the processor may determine a first likelihood that the aerial robot  610  is in the first region  604 . The processor may determine a second likelihood that the aerial robot  610  is in the second region  606 . The processor may determine that the aerial robot is the transition region  640  based on the first likelihood and the second likelihood. For instance, if both the first likelihood indicates that the aerial robot  610  is unlikely to be in the first region  604  and the second likelihood indicates that the aerial robot  610  is unlikely to be in the second region  606 , the process may determine that the aerial robot  610  is in the transition region  640 . 
     In yet another embodiment, the transition region may be defined based on the presence of an obstacle. For example, the processor may determine whether an obstacle is present based on the distance readings from the distance sensors. The processor may determine an average of distance readings from the data of the distance sensor, such as an average of the time series distance data from a period preceding the latest value. The processor may determine a difference between the average and a particular distance reading at a particular instance, such as the latest instance. In response to the difference being larger than a threshold, the processor may determine that an obstacle likely is present at the particular instance because there is a sudden change in distance reading that is rather significant. The processor may, in turn, determine that the aerial robot  610  has entered a transition region until the readings from the distance sensor become stable again. 
     In yet another embodiment, the transition may be defined based on any suitable combinations of criteria mentioned above or another criterion that is not explicitly discussed. 
     The processor determines  665  a second height estimate  632  of the aerial robot  610  using data from the visual inertial sensor for at least a part of the duration in which the aerial robot  610  is in the transition region  640 . At the transition region  640 , the sudden change in surface levels from the first surface level  605  to the second surface level  607  prevents the distance senor from accurately determining the second height estimate  632  because the signal of the distance sensor cannot penetrate an obstacle and travel to the first surface level  605 . Instead of using the data of the distance sensor, the aerial robot  610  switches to the data of the visual inertial sensor. However, as explained above, there may be biases between the readings of the distance sensor and the readings of the visual inertial sensor. The processor may determine the visual inertial bias. For example, the visual inertial bias may be determined from an average of the readings of the visual inertial sensor from a period preceding the transition region  640 , such as the period during which the aerial robot  610  is in the first region  604 . In determining the second height estimate  632 , the processor receives a reading from the data of the visual inertial sensor. The processor determines the second height estimate  632  using the reading adjusted by the visual inertial bias. 
     The processor controls  670  the flight of the aerial robot  610  using the second height estimate  632  in the transition region  640 . The size of the transition region  640  may depend on various factors as discussed in step  660 . When traveling in the transition region  640  or immediately after the transition region  640 , the processor may determine a distance sensor bias. For example, in the transition region, the visual inertial sensor may be providing the second height estimate  632  while the distance sensor may be providing a distance reading D because the signal of the distance sensor is reflected at the second surface level  607 . As such, the distance sensor bias may be the difference between the second height estimate  632  and the distance reading D, which is approximately equal to the difference between the first surface level  605  and the second surface level  607 . 
     Based on one or more factors that define a transition region as discussed above in step  660 , the processor may determine that the aerial robot  610  has exited a transition region. For example, the processor determines  675  that the aerial robot  610  is in the second region  606  for more than a threshold period of time. The threshold period of time may be of a predetermined length or may be measured based on the stability of the data of the distance sensor. The processor reverts  680  to using the data from the distance sensor to determine a third height estimate  634  of the aerial robot  610  during which the aerial robot  610  is in the second region  606 . In using the data of the distance sensor to determine the third height estimate  634 , the processor may adjust the data using the distance sensor bias. For example, the processor may add the distance sensor bias to the distance readings from the distance sensor. 
     The aerial robot  610  may continue to travel to the third region  608  and back to the second region  606  via the transition region  642  and the transition region  644 . The aerial robot  610  may repeat the process of switching between the data from the distance sensor and the data from the visual inertial sensor and monitoring the various biases between the two sets of data. 
     Example Height Estimation Algorithm 
       FIG.  7 A  is a block diagram illustrating an example height estimate algorithm  700 , according to an embodiment. The height estimate algorithm  700  may be an example algorithm that may be used to perform the height estimate process illustrated in  FIG.  6 C . The height estimate algorithm  700  is merely one example for performing the process described in  FIG.  6 C . In various embodiments, the process described in  FIG.  6 C  may also be performed using other algorithms. The height estimate algorithm  700  may be part of the algorithm used in state estimator  235  such as the height estimator  238 . The height estimate algorithm  700  may be carried by a general processor that executes code instructions saved in a memory or may be programmed in a special-purpose processor, depending on the design of an aerial robot  610 . 
     The height estimate algorithm  700  may include various functions for making different determinations. For example, the height estimate algorithm  700  may include an obstacle detection function  710 , a downward status detection function  720 , a visual inertial bias correction function  730 , a distance sensor bias correction function  740 , and a sensor selection and publication function  750 . In various embodiments, the height estimate algorithm  700  may include different, fewer, or additional functions. Functions may also be combined or further separated. The determinations made by each function may also be distributed among various functions in a different manner described in  FIG.  7 A . 
     The flow described in the height estimate algorithm  700  may correspond to a particular instance in time. The processor of an aerial robot  610  may repeat the height estimate algorithm  700  to generate one or more time series of data. The height estimate algorithm  700  may receive distance sensor data  760 , pose data  770 , and visual inertial data  780  as inputs and generate the height estimate  790  as the output. The distance sensor data  760  may include m r , which may be the distance reading from a distance sensor, such as the distance reading as indicated by line  624  shown in  FIG.  6 B . The pose data  770  may include {circumflex over (z)}, {circumflex over (ϕ)} and {circumflex over (θ)}, which are generated from the state estimator  235 . {circumflex over (z)} may be the height estimate generated by the state estimator  235 . For example, {circumflex over (z)} may be the estimate value on z-axis. Typically, z-axis measures upward from the start surface to the robot and measures downward from the robot to the start surface. As such, {circumflex over (z)} may be the robot height estimate from the start surface. {circumflex over (ϕ)} may be the roll angle of the aerial robot  610 . {circumflex over (θ)} may be the pitch angle of the aerial robot  610 . The visual inertial data  780  may include m v , which may be the height reading from the visual inertial sensor. The height estimate algorithm  700  generates the final height estimate  790 , denoted as z. 
     The obstacle detection function  710  may determine whether an obstacle is detected based on the pose data  770  {circumflex over (z)}, {circumflex over (ϕ)} and {circumflex over (θ)}, and the distance sensor data  760  m r . For example, the obstacle detection function  710  may determine whether the distance reading from the distance data  760  and the distance reading calculated from the pose data  770  agree (e.g., the absolute difference or square difference between the two readings is less than or larger than a threshold). If the two data sources agree, the obstacle detection function  710  may generate a first label as the output of the obstacle detection function  710 . The first label denotes that an obstacle is not detected. If the two data sources do not agree, the obstacle detection function  710  may generate a second label as the output, which denotes that an obstacle is detected. The obstacle detection function  710  may be represented by the following mathematical equations.  1 G may be the output of the obstacle detection function  710 . 
     
       
         
           
             
               1 
               G 
             
             = 
             
               { 
               
                 
                   
                     1 
                   
                   
                     
                       
                         if 
                         ⁢ 
                             
                         d 
                       
                       &lt; 
                       
                         G 
                         2 
                       
                     
                   
                 
                 
                   
                     0 
                   
                   
                     
                       
                         if 
                         ⁢ 
                             
                         d 
                       
                       &gt; 
                       
                         G 
                         2 
                       
                     
                   
                 
               
             
           
         
       
     
     where, 
         d= ( m   r   −     m   r   ) 2    
           m     r   ={circumflex over (z)} /(cos({circumflex over (ϕ)})*sin({circumflex over (θ)}))
 
     The downward status detection function  720  may include one or more probabilities model to determine the likelihood P(H 1 ) that the aerial robot  610  is flying over a first region (e.g., the floor) and the likelihood P(H 2 ) that the aerial robot  610  is flying over a second region (e.g., on top of an obstacle). The downward status detection function  720  assigns a state S to the aerial robot  610 . The state may correspond to the first region, the second region, or a transition region. For example, if the likelihood P(H 1 ) and likelihood P(H 2 ) indicate that the aerial robot  610  is neither in the first region nor the second region, the downward status detection function  720  assigns that the aerial robot  610  is in the transition region. The downward status detection function  720  may be represented by the following mathematical equations. 
     
       
         
           
             S 
             = 
             
               { 
               
                 
                   
                     
                       0 
                       ⁢ 
                       
                         ( 
                         floor 
                         ) 
                       
                     
                   
                   
                     
                       ( 
                       
                         
                           if 
                           ⁢ 
                               
                           
                             P 
                             ⁡ 
                             ( 
                             
                               H 
                               1 
                             
                             ) 
                           
                         
                         ≥ 
                         0.8 
                       
                       ) 
                     
                   
                 
                 
                   
                     
                       1 
                       ⁢ 
                       
                         ( 
                         obstacle 
                         ) 
                       
                     
                   
                   
                     
                       ( 
                       
                         
                           if 
                           ⁢ 
                               
                           
                             P 
                             ⁡ 
                             ( 
                             
                               H 
                               2 
                             
                             ) 
                           
                         
                         &gt; 
                         0.2 
                       
                       ) 
                     
                   
                 
                 
                   
                     
                       2 
                       ⁢ 
                       
                         ( 
                         transition 
                         ) 
                       
                     
                   
                   
                     
                       ( 
                       otherwise 
                       ) 
                     
                   
                 
               
             
           
         
       
     
     where 
     
       
         
           
             
               P 
               ⁡ 
               ( 
               H 
               ) 
             
             = 
             
               
                 
                   M 
                   
                     1 
                     G 
                   
                 
                 ⊗ 
                 
                   P 
                   ⁡ 
                   ( 
                   H 
                   ) 
                 
               
               
                 
                   M 
                   
                     1 
                     G 
                   
                   T 
                 
                 · 
                 
                   P 
                   ⁡ 
                   ( 
                   H 
                   ) 
                 
               
             
           
         
       
       
         
           
             
               H 
               = 
               
                 [ 
                 
                   
                     H 
                     1 
                   
                   , 
                   
                     H 
                     2 
                   
                 
                 ] 
               
             
             , 
           
         
       
       
         
           
             
               H 
               1 
             
             : 
                 
             robot 
             ⁢ 
                 
             is 
             ⁢ 
                 
             on 
             ⁢ 
                 
             top 
             ⁢ 
                 
             of 
             ⁢ 
                 
             the 
             ⁢ 
                 
             floor 
           
         
       
       
         
           
             
               H 
               2 
             
             : 
                 
             robot 
             ⁢ 
                 
             is 
             ⁢ 
               
             on 
             ⁢ 
                 
             top 
             ⁢ 
                 
             of 
             ⁢ 
                 
             an 
             ⁢ 
                 
             obstacle 
           
         
       
       
         
           
             
               M 
               
                 1 
                 G 
               
             
             : 
                 
             
               1 
               G 
               th 
             
             ⁢ 
                 
             column 
             ⁢ 
                 
             of 
             ⁢ 
                 
             matrix 
             ⁢ 
                 
             M 
           
         
       
       
         
           
             M 
             = 
             
               [ 
               
                 
                   
                     0.8 
                   
                   
                     0.2 
                   
                 
                 
                   
                     0.2 
                   
                   
                     0.8 
                   
                 
               
               ] 
             
           
         
       
     
     The visual inertial bias correction function  730  monitors the averaged bias of the visual inertial data  780  m v  relative to the distance sensor data  760  m r . As discussed above, data from a visual inertial sensor is prone to errors from drifts. The data from the visual inertial sensor may also have a constant bias compared to the data from the distance sensor. The aerial robot  610  monitors the visual inertial data  780  and determines the average of the visual inertial data  780  over a period of time. The average may be used to determine the visual inertial bias and corrects the visual inertial data  780  based on the bias. The visual inertial bias correction function  730  may be represented by the following mathematical equations. b z (k) denotes the visual inertial bias and MA denotes a moving average. {circumflex over (m)} v,z (k) denotes the adjusted visual inertial data.
     If S=0,   

         {circumflex over (m)}   v,z ( k )=MA( m   v,z ( k−n:k )) 
         b   z ( k )={circumflex over (m)} v,z ( k )− m   r ( k )cos(ϕ))cos(θ)
 
         {hacek over (m)}   v,z ( k )= {circumflex over (m)}   v,z ( k )− b   z ( k )
 
     The distance sensor bias correction function  740  compensates the distance sensor data  760  from the distance sensor when the aerial robot  610  is flying over an obstacle. The values of the distance sensor data  760  may become smaller than the actual height because signals from the distance sensor are unable to reach the ground due to the presence of an obstacle. The distance sensor bias correction function  740  makes the adjustment when the aerial robot  610  reverts to using the distance sensor to estimate height after a transition region. The distance sensor bias correction function  740  may be represented by the following mathematical equations. b r (k) denotes the distance sensor bias and {circumflex over (m)} r (k) denotes the adjusted distance sensor data. 
     If S=1 and t s=1 &lt;ε, (on obstacle) 
       {hacek over (m)} r ( k )=m r ( k )− b   r ( k )
 
     where 
     
       
         
           
             
               
                 b 
                 r 
               
               ( 
               k 
               ) 
             
             = 
             
               
                 
                   m 
                   r 
                 
                 ( 
                 k 
                 ) 
               
               - 
               
                 
                   
                     m 
                     ︶ 
                   
                   v 
                 
                 
                   
                     cos 
                     ⁡ 
                     ( 
                     
                       ϕ 
                       ^ 
                     
                     ) 
                   
                   ⁢ 
                   
                     cos 
                     ⁡ 
                     ( 
                     
                       θ 
                       ^ 
                     
                     ) 
                   
                 
               
             
           
         
       
       
         
           
             
               
                 t 
                 
                   S 
                   = 
                   1 
                 
               
               : 
                   
               elapsed 
               ⁢ 
                   
               time 
               ⁢ 
                   
               after 
               ⁢ 
                   
               s 
             
             = 
             1 
           
         
       
     
     The sensor selection and publication function  750  selects the sensor used in various situations and generate the final determination of the height estimate z. For example, in one embodiment, if the aerial robot  610  is in the first region, the aerial robot  610  uses the distance sensor data  760  to determine the height estimate z. If the aerial robot  610  is in the transition region, the aerial robot  610  uses the visual inertial data  780 . If the aerial robot  610  is in the second region (e.g., on top of an obstacle) after the transition region within a threshold period of time, the aerial robot  610  may also use the visual inertial data  780 . Afterward, the aerial robot  610  reverts to using the distance sensor data  760 . The sensor selection and publication function  750  may be represented by the following pseudocode. 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 If S = 0, (on floor) 
               
               
                   
                      z = m r  cos(ϕ) cos(θ) 
               
               
                   
                 Else if S = 1, (on obstacle) 
               
               
                   
                   If t S=1  &lt; ε 
               
               
                   
                       z = {hacek over (m)} v,z   
               
               
                   
                   else, 
               
               
                   
                        z = {hacek over (m)} r   
               
               
                   
                 Else if S = 2, (transition) 
               
               
                   
                       z = {hacek over (m)} v,z   
               
               
                   
                   
               
            
           
         
       
     
     The height estimate algorithm  700  provides an example of estimating heights of an aerial robot that may be implemented at a site that has a layer of obstacles. In various embodiments, similar principles may be expanded for multiple layers of obstacles. 
       FIG.  7 B  is a conceptual diagram illustrating the use of different functions of the height estimate algorithm  700  and sensor data used as an aerial robot  610  flies over an obstacle and maintains a level flight, according to an embodiment. The obstacle detection function  710 , the downward status decision function  720 , and the sensor selection and publication function  750  are used throughout the process. In the region  792  in which the aerial robot  610  is flying on top of the first region (e.g., the floor), distance sensor data  760  is used because the readings from the distance sensor should be relatively stable. The visual inertial bias correction function  730  is also run to monitor the bias of the visual inertial data  780 . In the transition region  794 , the visual inertial data  780  is used instead of the distance sensor data  760  because the distance sensor data  760  may become unstable when the boundary of the obstacle causes a sudden change in the distance sensor data  760 . 
     Shortly after the transition region  794  and within the threshold ε  796 , the aerial robot  610  may determine that the distance sensor data  760  may become stable again. In this period, the aerial robot  610  may continue to use the visual inertial data  780  and may run the distance sensor bias correction function  740  to determine a compensation value that should be added to the distance sensor data  760  to account for the depth of the obstacle. When the aerial robot  610  is in the second region  798  (e.g., on top of the obstacle) and the aerial robot  610  also determines that it is ready to switch back to the distance sensor (e.g., the data of the distance sensor is stable again), the aerial robot  610  uses the distance sensor data  760  to estimate the height again, with an adjustment by the distance sensor bias. The aerial robot  610  also runs the visual inertial bias correction function  730  again to monitor the bias of the visual inertial data  780 . The process may continue in a similar manner as the aerial robot  610  travel across different surface levels. 
     Example Machine Learning Models 
     In various embodiments, a wide variety of machine learning techniques may be used. Examples include different forms of supervised learning, unsupervised learning, and semi-supervised learning such as decision trees, support vector machines (SVMs), regression, Bayesian networks, and genetic algorithms. Deep learning techniques such as neural networks, including convolutional neural networks (CNN), recurrent neural networks (RNN) and long short-term memory networks (LSTM), may also be used. For example, various object recognitions performed by visual reference engine  240 , localization, and other processes may apply one or more machine learning and deep learning techniques. 
     In various embodiments, the training techniques for a machine learning model may be supervised, semi-supervised, or unsupervised. In supervised learning, the machine learning models may be trained with a set of training samples that are labeled. For example, for a machine learning model trained to classify objects, the training samples may be different pictures of objects labeled with the type of objects. The labels for each training sample may be binary or multi-class. In training a machine learning model for image segmentation, the training samples may be pictures of regularly shaped objects in various storage sites with segments of the images manually identified. In some cases, an unsupervised learning technique may be used. The samples used in training are not labeled. Various unsupervised learning technique such as clustering may be used. In some cases, the training may be semi-supervised with training set having a mix of labeled samples and unlabeled samples. 
     A machine learning model may be associated with an objective function, which generates a metric value that describes the objective goal of the training process. For example, the training may intend to reduce the error rate of the model in generating predictions. In such a case, the objective function may monitor the error rate of the machine learning model. In object recognition (e.g., object detection and classification), the objective function of the machine learning algorithm may be the training error rate in classifying objects in a training set. Such an objective function may be called a loss function. Other forms of objective functions may also be used, particularly for unsupervised learning models whose error rates are not easily determined due to the lack of labels. In image segmentation, the objective function may correspond to the difference between the model&#39;s predicted segments and the manually identified segments in the training sets. In various embodiments, the error rate may be measured as cross-entropy loss, L1 loss (e.g., the sum of absolute differences between the predicted values and the actual value), L2 loss (e.g., the sum of squared distances). 
     Referring to  FIG.  8   , a structure of an example CNN is illustrated, in accordance with some embodiments. The CNN  800  may receive an input  810  and generate an output  820 . The CNN  800  may include different kinds of layers, such as convolutional layers  830 , pooling layers  840 , recurrent layers  850 , full connected layers  860 , and custom layers  870 . A convolutional layer  830  convolves the input of the layer (e.g., an image) with one or more kernels to generate different types of images that are filtered by the kernels to generate feature maps. Each convolution result may be associated with an activation function. A convolutional layer  830  may be followed by a pooling layer  840  that selects the maximum value (max pooling) or average value (average pooling) from the portion of the input covered by the kernel size. The pooling layer  840  reduces the spatial size of the extracted features. In some embodiments, a pair of convolutional layer  830  and pooling layer  840  may be followed by a recurrent layer  850  that includes one or more feedback loop  855 . The feedback  855  may be used to account for spatial relationships of the features in an image or temporal relationships of the objects in the image. The layers  830 ,  840 , and  850  may be followed in multiple fully connected layers  860  that have nodes (represented by squares in  FIG.  8   ) connected to each other. The fully connected layers  860  may be used for classification and object detection. In some embodiments, one or more custom layers  870  may also be presented for the generation of a specific format of output  820 . For example, a custom layer may be used for image segmentation for labeling pixels of an image input with different segment labels. 
     The order of layers and the number of layers of the CNN  800  in  FIG.  8    is for example only. In various embodiments, a CNN  800  includes one or more convolutional layer  830  but may or may not include any pooling layer  840  or recurrent layer  850 . If a pooling layer  840  is present, not all convolutional layers  830  are always followed by a pooling layer  840 . A recurrent layer may also be positioned differently at other locations of the CNN. For each convolutional layer  830 , the sizes of kernels (e.g., 3×3, 5×5, 7×7, etc.) and the numbers of kernels allowed to be learned may be different from other convolutional layers  830 . 
     A machine learning model may include certain layers, nodes, kernels and/or coefficients. Training of a neural network, such as the CNN  800 , may include forward propagation and backpropagation. Each layer in a neural network may include one or more nodes, which may be fully or partially connected to other nodes in adjacent layers. In forward propagation, the neural network performs the computation in the forward direction based on outputs of a preceding layer. The operation of a node may be defined by one or more functions. The functions that define the operation of a node may include various computation operations such as convolution of data with one or more kernels, pooling, recurrent loop in RNN, various gates in LSTM, etc. The functions may also include an activation function that adjusts the weight of the output of the node. Nodes in different layers may be associated with different functions. 
     Each of the functions in the neural network may be associated with different coefficients (e.g. weights and kernel coefficients) that are adjustable during training. In addition, some of the nodes in a neural network may also be associated with an activation function that decides the weight of the output of the node in forward propagation. Common activation functions may include step functions, linear functions, sigmoid functions, hyperbolic tangent functions (tanh), and rectified linear unit functions (ReLU). After an input is provided into the neural network and passes through a neural network in the forward direction, the results may be compared to the training labels or other values in the training set to determine the neural network&#39;s performance. The process of prediction may be repeated for other images in the training sets to compute the value of the objective function in a particular training round. In turn, the neural network performs backpropagation by using gradient descent such as stochastic gradient descent (SGD) to adjust the coefficients in various functions to improve the value of the objective function. 
     Multiple rounds of forward propagation and backpropagation may be performed. Training may be completed when the objective function has become sufficiently stable (e.g., the machine learning model has converged) or after a predetermined number of rounds for a particular set of training samples. The trained machine learning model can be used for performing prediction, object detection, image segmentation, or another suitable task for which the model is trained. 
     Computing Machine Architecture 
       FIG.  9    is a block diagram illustrating components of an example computing machine that is capable of reading instructions from a computer-readable medium and execute them in a processor (or controller). A computer described herein may include a single computing machine shown in  FIG.  9   , a virtual machine, a distributed computing system that includes multiples nodes of computing machines shown in  FIG.  9   , or any other suitable arrangement of computing devices. 
     By way of example,  FIG.  9    shows a diagrammatic representation of a computing machine in the example form of a computer system  900  within which instructions  924  (e.g., software, program code, or machine code), which may be stored in a computer-readable medium for causing the machine to perform any one or more of the processes discussed herein may be executed. In some embodiments, the computing machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a network deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. 
     The structure of a computing machine described in  FIG.  9    may correspond to any software, hardware, or combined components shown in  FIGS.  1  and  2   , including but not limited to, the inventory management system  140 , the computing server  150 , the data store  160 , the user device  170 , and various engines, modules, interfaces, terminals, and machines shown in  FIG.  2   . While  FIG.  9    shows various hardware and software elements, each of the components described in  FIGS.  1  and  2    may include additional or fewer elements. 
     By way of example, a computing machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a smartphone, a web appliance, a network router, an internet of things (IoT) device, a switch or bridge, or any machine capable of executing instructions  924  that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute instructions  924  to perform any one or more of the methodologies discussed herein. 
     The example computer system  900  includes one or more processors (generally, processor  902 ) (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application-specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any combination of these), a main memory  904 , and a non-volatile memory  906 , which are configured to communicate with each other via a bus  908 . The computer system  900  may further include graphics display unit  910  (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)). The computer system  900  may also include alphanumeric input device  912  (e.g., a keyboard), a cursor control device  914  (e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument), a storage unit  916 , a signal generation device  918  (e.g., a speaker), and a network interface device  920 , which also are configured to communicate via the bus  908 . 
     The storage unit  916  includes a computer-readable medium  922  on which is stored instructions  924  embodying any one or more of the methodologies or functions described herein. The instructions  924  may also reside, completely or at least partially, within the main memory  904  or within the processor  902  (e.g., within a processor&#39;s cache memory) during execution thereof by the computer system  900 , the main memory  904  and the processor  902  also constituting computer-readable media. The instructions  924  may be transmitted or received over a network  926  via the network interface device  920 . 
     While computer-readable medium  922  is shown in an example embodiment to be a single medium, the term “computer-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions  924 ). The computer-readable medium may include any medium that is capable of storing instructions (e.g., instructions  924 ) for execution by the machine and that cause the machine to perform any one or more of the methodologies disclosed herein. The computer-readable medium may include, but not be limited to, data repositories in the form of solid-state memories, optical media, and magnetic media. The computer-readable medium does not include a transitory medium such as a signal or a carrier wave. 
     Additional Configuration Considerations 
     Certain embodiments are described herein as including logic or a number of components, engines, modules, or mechanisms. Engines may constitute either software modules (e.g., code embodied on a computer-readable medium) or hardware modules. A hardware engine is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware engines of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware engine that operates to perform certain operations as described herein. 
     In various embodiments, a hardware engine may be implemented mechanically or electronically. For example, a hardware engine may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware engine may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or another programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware engine mechanically, in dedicated and permanently configured circuitry, or temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors, e.g., processor  902 , that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented engines that operate to perform one or more operations or functions. The engines referred to herein may, in some example embodiments, comprise processor-implemented engines. 
     The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations. 
     Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for a similar system or process through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes, and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.