Patent Publication Number: US-11662805-B2

Title: Periodic parameter estimation for visual-inertial tracking systems

Description:
CROSS REFERENCE 
     The present application claims priority to U.S. Provisional Patent Application Ser. No. 63/131,981, filed Dec. 30, 2020, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The subject matter disclosed herein generally relates to a visual tracking system. Specifically, the present disclosure addresses systems and methods for calibrating visual-inertial tracking systems. 
     BACKGROUND 
     An augmented reality (AR) device enables a user to observe a scene while simultaneously seeing relevant virtual content that may be aligned to items, images, objects, or environments in the field of view of the device. A virtual reality (VR) device provides a more immersive experience than an AR device. The VR device blocks out the field of view of the user with virtual content that is displayed based on a position and orientation of the VR device. 
     Both AR and VR devices rely on motion tracking systems that track a pose (e.g., orientation, position, location) of the device. A motion tracking system is typically factory calibrated (based on predefined/known relative positions between the cameras and other sensors) to accurately display the virtual content at a desired location relative to its environment. However, factory calibration parameters can drift over time as the user wears the AR/VR device due to mechanical stress and temperature changes in the AR/VR device. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. 
         FIG.  1    is a block diagram illustrating an environment for operating an AR/VR display device in accordance with one example embodiment. 
         FIG.  2    is a block diagram illustrating an AR/VR display device in accordance with one example embodiment. 
         FIG.  3    is a block diagram illustrating a visual inertial tracking system in accordance with one example embodiment. 
         FIG.  4    is a block diagram illustrating a tracking calibration module in accordance with one example embodiment. 
         FIG.  5    is a flow diagram illustrating a parameter estimation operation in accordance with one example embodiment. 
         FIG.  6    is a block diagram illustrating a process in accordance with one example embodiment. 
         FIG.  7    is a flow diagram illustrating a method for storing the latest estimated parameter value in accordance with one example embodiment. 
         FIG.  8    is a flow diagram illustrating a method for calibrating a visual inertial tracking system in accordance with one example embodiment. 
         FIG.  9    is a flow diagram illustrating a method for triggering the visual internal tracking system in accordance with one example embodiment. 
         FIG.  10    is a flow diagram illustrating a method for calibrating a visual-inertial tracking system in accordance with one example embodiment. 
         FIG.  11    is block diagram showing a software architecture within which the present disclosure may be implemented, according to an example embodiment. 
         FIG.  12    is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to one example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The description that follows describes systems, methods, techniques, instruction sequences, and computing machine program products that illustrate example embodiments of the present subject matter. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the present subject matter. It will be evident, however, to those skilled in the art, that embodiments of the present subject matter may be practiced without some or other of these specific details. Examples merely typify possible variations. Unless explicitly stated otherwise, structures (e.g., structural Components, such as modules) are optional and may be combined or subdivided, and operations (e.g., in a procedure, algorithm, or other function) may vary in sequence or be combined or subdivided. 
     Both AR and VR applications allow a user to access information, such as in the form of virtual content rendered in a display of an AR/VR display device (also referred to as a display device). The rendering of the virtual content may be based on a position of the display device relative to a physical object or relative to a frame of reference (external to the display device) so that the virtual content correctly appears in the display. For AR, the virtual content appears aligned with a physical object as perceived by the user and a camera of the AR display device. The virtual content appears to be attached to the physical world (e.g., a physical object of interest). In order to do this, the AR display device detects the physical object and tracks a pose of the AR display device relative to a position of the physical object. A pose identifies a position and orientation of the display device relative to a frame of reference or relative to another object. For VR, the virtual object appears at a location based on the pose of the VR display device. The virtual content is therefore refreshed based on the latest pose of the device. 
     A tracking system (also referred to as a visual-inertial tracking system) at the display device determines the latest position or pose of the display device. An example of a tracking system includes a visual-inertial tracking system (also referred to as visual odometry system) that relies on data acquired from multiple sensors (e.g., depth cameras, inertial sensors). The tracking system calibrates the sensors to accurately determine the pose of the display device. The calibrated parameters include extrinsic parameters (e.g., relative orientations and positions between sensors), and intrinsic parameters (e.g., internal camera or lens parameters). 
     Although the tracking system of the display device is factory calibrated, these (extrinsic/intrinsic) parameters may change over time (e.g., due to mechanical stress, temperature changes). The tracking system mitigates the changes by gradually updating the parameter values during runtime of the AR/VR application. However, the more the parameter values have diverged from the factory calibration, the longer it takes for the tracking system to “catch up” and obtain an accurate new estimate of the parameter values (e.g., the convergence time becomes longer). Keeping this convergence time short is important, because as long as the estimates are inaccurate, tracking performance is negatively impacted. Furthermore, the parameter values may be used by components other than the tracking system. For applications that rely on these components (e.g., VR/AR systems), it is important that the tracking system operates accurately right from the start (e.g., when the AR/VR application starts or is online). 
     The present application describes a method for reducing the convergence time for online parameter estimation at the start of a tracking system. Instead of just operating the tracking system whenever requested by the AR/VR application, the tracking system is periodically started and run for a short time (e.g., just until a new parameter estimation is obtained). This new parameter value estimation is then stored and later reused as a more up-to-date value (e.g., a new starting value for parameter estimation). Therefore, the last up-to-date parameter value is generally a more accurate starting point for parameter estimation when the tracking system is started the next time. Starting from the last up-to-date parameter value leads to a reduced convergence time and higher tracking accuracy immediately after the start of the tracking system. 
     In one example embodiment, the present application describes a method for calibrating a visual-inertial tracking system comprising: operating, at a device, the visual-inertial tracking system without receiving a tracking request from a virtual object display application; in response to operating the visual-inertial tracking system, accessing sensor data from a plurality of sensors of the device; identifying, based on the sensor data, a first calibration parameter value of the visual-inertial tracking system; storing the first calibration parameter value; detecting the tracking request from the virtual object display application to the visual-inertial tracking system; and in response to detecting the tracking request, accessing the first calibration parameter value and determining a second calibration parameter value from the first calibration parameter value. 
     As a result, one or more of the methodologies described herein facilitate solving the technical problem of power consumption saving and efficient calibration by periodically operating the tracking system to determine a parameter value, storing the latest parameter value, and using the last parameter value as a starting point the next time the tracking system is requested by an application. The presently described method provides an improvement to an operation of the functioning of a computer by providing power consumption reduction and fastest calibration computation. As such, one or more of the methodologies described herein may obviate a need for certain efforts or computing resources. Examples of such computing resources include Processor cycles, network traffic, memory usage, data storage capacity, power consumption, network bandwidth, and cooling capacity. 
       FIG.  1    is a network diagram illustrating an environment  100  suitable for operating an AR/VR display device  106 , according to some example embodiments. The environment  100  includes a user  102 , an AR/VR display device  106 , and a physical object  104 . A user  102  operates the AR/VR display device  106 . The user  102  may be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the AR/VR display device  106 ), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The user  102  is associated with the AR/VR display device  106 . 
     The AR/VR display device  106  may be a computing device with a display such as a smartphone, a tablet computer, or a wearable computing device (e.g., watch or glasses). The computing device may be hand-held or may be removable mounted to a head of the user  102 . In one example, the display includes a screen that displays images captured with a camera of the AR/VR display device  106 . In another example, the display of the device may be transparent such as in lenses of wearable computing glasses. In other examples, the display may be non-transparent, partially transparent, partially opaque. In yet other examples, the display may be wearable by the user  102  to cover the field of vision of the user  102 . 
     The AR/VR display device  106  includes an AR application generates virtual content based on images detected with the camera of the AR/VR display device  106 . For example, the user  102  may point a camera of the AR/VR display device  106  to capture an image of the physical object  104 . The AR application generates virtual content corresponding to an identified object (e.g., physical object  104 ) in the image and presents the virtual content in a display of the AR/VR display device  106 . 
     The AR/VR display device  106  includes a visual inertial tracking system  108 . The visual inertial tracking system  108  tracks the pose (e.g., position and orientation) of the AR/VR display device  106  relative to the real-world environment  110  using, for example, optical sensors (e.g., depth-enabled 3D camera, image camera), inertia sensors (e.g., gyroscope, accelerometer), wireless sensors (Bluetooth, Wi-Fi), GPS sensor, and audio sensor. In one example, the AR/VR display device  106  displays virtual content based on the pose of the AR/VR display device  106  relative to the real-world environment  110  and/or the physical object  104 . 
     Any of the machines, databases, or devices shown in  FIG.  1    may be implemented in a general-purpose computer modified (e.g., configured or programmed) by software to be a special-purpose computer to perform one or more of the functions described herein for that machine, database, or device. For example, a computer system able to implement any one or more of the methodologies described herein is discussed below with respect to  FIG.  7    to  FIG.  10   . As used herein, a “database” is a data storage resource and may store data structured as a text file, a table, a spreadsheet, a relational database (e.g., an object-relational database), a triple store, a hierarchical data store, or any suitable combination thereof. Moreover, any two or more of the machines, databases, or devices illustrated in  FIG.  1    may be combined into a single machine, and the functions described herein for any single machine, database, or device may be subdivided among multiple machines, databases, or devices. 
     The AR/VR display device  106  may operate over a computer network. The computer network may be any network that enables communication between or among machines, databases, and devices. Accordingly, the computer network may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The computer network may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof. 
       FIG.  2    is a block diagram illustrating modules (e.g., components) of the AR/VR display device  106 , according to some example embodiments. The AR/VR display device  106  includes sensors  202 , a display  204 , a processor  208 , and a storage device  206 . Examples of AR/VR display device  106  include a wearable computing device, a mobile computing device, a navigational device, a portable media device, or a smart phone. 
     The sensors  202  include, for example, an optical sensor  212  (e.g., camera such as a color camera, a thermal camera, a depth sensor and one or multiple grayscale, global/rolling shutter tracking cameras) and an inertial sensor  214  (e.g., gyroscope, accelerometer). Other examples of sensors  202  include a proximity or location sensor (e.g., near field communication, GPS, Bluetooth, Wi-Fi), an audio sensor (e.g., a microphone), or any suitable combination thereof. It is noted that the sensors  202  described herein are for illustration purposes and the sensors  202  are thus not limited to the ones described above. 
     The display  204  includes a screen or monitor configured to display images generated by the processor  208 . In one example embodiment, the display  204  may be transparent or semi-opaque so that the user  102  can see through the display  204  (in AR use case). In another example embodiment, the display  204  covers the eyes of the user  102  and blocks out the entire field of view of the user  102  (in VR use case). In another example, the display  204  includes a touchscreen display configured to receive a user input via a contact on the touchscreen display. 
     The processor  208  includes an AR/VR application  210 , a visual inertial tracking system  108 , and a tracking calibration module  216 . The AR/VR application  210  detects and identifies a physical environment or the physical object  104  using computer vision. The AR/VR application  210  retrieves virtual content (e.g., 3D object model) based on the identified physical object  104  or physical environment. The AR/VR application  210  renders the virtual object in the display  204 . In one example embodiment, the AR/VR application  210  includes a local rendering engine that generates a visualization of virtual content overlaid (e.g., superimposed upon, or otherwise displayed in tandem with) on an image of the physical object  104  captured by the optical sensor  212 . A visualization of the virtual content may be manipulated by adjusting a position of the physical object  104  (e.g., its physical location, orientation, or both) relative to the optical sensor  212 . Similarly, the visualization of the virtual content may be manipulated by adjusting a pose of the AR/VR display device  106  relative to the physical object  104 . For a VR application, the AR/VR application  210  displays the virtual content in the display  204  at a location (in the display  204 ) determined based on a pose of the AR/VR display device  106 . 
     The visual inertial tracking system  108  estimates a pose of the AR/VR display device  106 . For example, the visual inertial tracking system  108  uses image data and corresponding inertial data from the optical sensor  212  and the inertial sensor  214  to track a location and pose of the AR/VR display device  106  relative to a frame of reference (e.g., real world environment  110 ). In one example embodiment, the visual inertial tracking system  108  operates independently and asynchronously from the AR/VR application  210 . For example, the visual inertial tracking system  108  operates offline without receiving any tracking request from the AR/VR application  210 . In another example, the visual inertial tracking system  108  operates periodically (e.g., every n second, m minutes) regardless of whether the AR/VR application  210  is running at the AR/VR display device  106 . 
     The tracking calibration module  216  initially calibrates the internal visual odometry system (e.g., optical sensor  212 , inertial sensor  214 ) of the visual inertial tracking system  108  based on default calibration parameter values (e.g., factory calibration). When the AR/VR application  210  operates, the visual inertial tracking system  108  may be referred to as online. When the AR/VR application  210  stops operating, the visual inertial tracking system  108  may be referred to as offline. 
     In one example embodiment, instead of running the visual inertial tracking system  108  whenever requested by the AR/VR application  210 , the tracking calibration module  216  directs the visual inertial tracking system  108  to periodically start and run for a short period of time until a new parameter estimation is obtained. The tracking calibration module  216  stores the new estimation in the storage device  206 . The tracking calibration module  216  re-uses the new estimation as a more up-to-date starting point for another parameter estimation when the visual inertial tracking system  108  is started the next time. Using the new estimation as a starting point for another parameter estimation leads to a reduced convergence time and higher tracking accuracy immediately after the start of the visual inertial tracking system  108 . 
     The storage device  206  stores virtual content  218  and saved online estimated parameters  220 . The virtual content  218  includes, for example, a database of visual references (e.g., images of physical objects) and corresponding experiences (e.g., three-dimensional virtual object models). The saved online estimated parameters  220  include, for example, the latest estimated parameter values for the visual inertial tracking system  108 . In one example, the saved online estimated parameters  220  updates the latest estimated parameter value based on the latest estimated parameter value determined by a periodic operation of the visual inertial tracking system  108 . 
     Any one or more of the modules described herein may be implemented using hardware (e.g., a Processor of a machine) or a combination of hardware and software. For example, any module described herein may configure a Processor to perform the operations described herein for that module. Moreover, any two or more of these modules may be combined into a single module, and the functions described herein for a single module may be subdivided among multiple modules. Furthermore, according to various example embodiments, modules described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices. 
       FIG.  3    illustrates the visual inertial tracking system  108  in accordance with one example embodiment. The visual inertial tracking system  108  includes, for example, an inertial sensor module  302 , an optical sensor module  304 , and a pose estimation module  306 . The inertial sensor module  302  accesses inertial sensor data from the inertial sensor  214 . The optical sensor module  304  accesses optical sensor data from the optical sensor  212 . 
     The pose estimation module  306  determines a pose (e.g., location, position, orientation) of the AR/VR display device  106  relative to a frame of reference (e.g., real world environment  110 ). In one example embodiment, the pose estimation module  306  includes a visual odometry system that estimates the pose of the AR/VR display device  106  based on 3D maps of feature points from images captured with the optical sensor  212  and the inertial sensor data captured with the inertial sensor  214 . The optical sensor module  304  accesses image data from the optical sensor  212 . 
     In one example embodiment, the pose estimation module  306  computes the position and orientation of the AR/VR display device  106 . The AR/VR display device  106  includes one or more optical sensor  212  mounted on a rigid platform (a frame of the AR/VR display device  106 ) with one or more inertial sensor  214 . The optical sensor  212  can be mounted with non-overlapping (distributed aperture) or overlapping (stereo or more) fields-of-view. 
     In some example embodiments, the pose estimation module  306  includes an algorithm that combines inertial information from the inertial sensor  214  and image information from the optical sensor  212  that are coupled to a rigid platform (e.g., AR/VR display device  106 ) or a rig. In one embodiment, a rig may consist of multiple cameras mounted on a rigid platform with an inertial navigation unit (e.g., inertial sensor  214 ). A rig may thus have at least one inertial navigation unit and at least one camera. 
       FIG.  4    is a block diagram illustrating a tracking calibration module  216  in accordance with one example embodiment. The tracking calibration module  216  includes an online parameter estimation component  402  and a periodic parameter estimation component  404 . 
     The online parameter estimation component  402  operates the visual inertial tracking system  108  in response to detecting that the AR/VR application  210  is requesting a tracking operation. In one example embodiment, the online parameter estimation component  402  accesses a latest estimated calibration parameter values stored in the saved online estimated parameters  220 . The online parameter estimation component  402  uses the latest estimated parameter values to calculate updated calibration parameter values. 
     The periodic parameter estimation component  404  periodically operates the visual inertial tracking system  108  to calculate the latest calibration parameter values. In one example, the periodic parameter estimation component  404  periodically operates the visual inertial tracking system  108  every n seconds. In another example, the periodic parameter estimation component  404  operates the visual inertial tracking system  108  in response to detecting a trigger event (e.g., temperature sensor indicates large change, accelerometer detects large change (e.g., device falling on the floor), “wear”-detector triggers (e.g., user putting on/wearing AR glasses, which causes mechanical stress on the frame), low battery power). The periodic parameter estimation component  404  stops the visual inertial tracking system  108  after the latest up-to-date estimated parameter value is available. The periodic parameter estimation component  404  stores the up-to-date estimated parameter value as the latest saved online estimated parameters  220  in storage device  206 . 
       FIG.  5    is a flow diagram illustrating a parameter estimation operation  516  in accordance with one example embodiment. Operations in the parameter estimation operation  516  may be performed by the visual inertial tracking system  108 , using Components (e.g., modules, engines) described above with respect to  FIG.  2    and  FIG.  3   . Accordingly, the parameter estimation operation  516  is described by way of example with reference to the visual inertial tracking system  108  and tracking calibration module  216 . However, it shall be appreciated that at least some of the operations may be deployed on various other hardware configurations or be performed by similar Components residing elsewhere. For example, some of the operations may be performed at the AR/VR application  210 . 
     At decision block  502 , the tracking calibration module  216  determines whether the AR/VR application  210  is requesting tracking operations from the visual inertial tracking system  108 . If the tracking calibration module  216  determines that the AR/VR application  210  is requesting tracking operations from the visual inertial tracking system  108 , the process proceeds to block  510 , block  512 , and decision block  514 . 
     At block  510 , the tracking calibration module  216  loads or retrieves a latest estimated parameter value from the storage device  206 . At block  512 , the tracking calibration module  216  calculates an updated latest calibration parameter value starting from the retrieved estimated parameter value. At decision block  514 , the tracking calibration module  216  determines whether the AR/VR application  210  still requires tracking operations from the visual inertial tracking system  108 . If the AR/VR application  210  does not need any further tracking operations from the visual inertial tracking system  108 , the process returns back to decision block  502 . 
     If the tracking calibration module  216  determines that the AR/VR application  210  is not requesting tracking operations from the visual inertial tracking system  108 , the process continues to decision block  504 , block  506 , and block  508 . At decision block  504 , the tracking calibration module  216  determines whether the re-estimate the latest parameter value based on a last operation of the AR/VR application  210 . At block  506 , the tracking calibration module  216  operates the visual inertial tracking system  108  offline (e.g., without running the AR/VR application  210 ). At block  508 , the tracking calibration module  216  saves the latest estimated parameter value and stops a tracking operation of the visual inertial tracking system  108 . 
       FIG.  6    is a block diagram illustrating an example process in accordance with one example embodiment. The visual inertial tracking system  108  receives sensor data from sensors  202  to determine a pose of the AR/VR display device  106 . The tracking calibration module  216  calibrates the sensor data based on the latest parameter estimated values (e.g., spatial relations between the cameras and IMU, IMU biases, bending of the frame resulting a displacement of sensors disposed at pre-defined locations in the AR/VR display device  106 , auto exposure). The tracking calibration module  216  stores the latest estimated parameter values (e.g., saved online estimated parameters  220 ) in the storage device  206 . 
     The calibrated spatial relations data is provided to a model  602  of the visual inertial tracking system  108 . The model  602  is initially determined with the factory calibration  604  and the data from the visual inertial tracking system  108 . In one example embodiment, the model  602  is determined based on data from the visual inertial tracking system  108  and the saved online estimated parameters  220 . The model  602  provides the geometric model of the AR/VR display device  106  to the AR/VR application  210 . 
     The AR/VR application  210  retrieves virtual content  218  from the storage device  206  and causes the virtual content  218  to be displayed at a location based on the geometric model of the AR/VR display device  106 . 
       FIG.  7    is a flow diagram illustrating a routine  700  for updating visual odometry of an AR display device in accordance with one example embodiment. Operations in the routine  700  may be performed by the visual inertial tracking system  108 , using Components (e.g., modules, engines) described above with respect to  FIG.  2   . Accordingly, the routine  700  is described by way of example with reference to the tracking calibration module  216 . However, it shall be appreciated that at least some of the operations of the routine  700  may be deployed on various other hardware configurations or be performed by similar Components residing elsewhere. 
     In block  702 , the periodic parameter estimation component  404  periodically operates the visual inertial tracking system  108 . In block  704 , the visual inertial tracking system  108  determines the latest estimated parameter value. In block  706 , the periodic parameter estimation component  404  stores the latest estimated parameter value. 
     It is to be noted that other embodiments may use different sequencing, additional or fewer operations, and different nomenclature or terminology to accomplish similar functions. In some embodiments, various operations may be performed in parallel with other operations, either in a synchronous or asynchronous manner. The operations described herein were chosen to illustrate some principles of operations in a simplified form. 
       FIG.  8    is a flow diagram illustrating a routine  800  for updating visual odometry of an AR display device in accordance with one example embodiment. Operations in the routine  800  may be performed by the tracking calibration module  216 , using Components (e.g., modules, engines) described above with respect to  FIG.  2   . Accordingly, the routine  800  is described by way of example with reference to the tracking calibration module  216 . However, it shall be appreciated that at least some of the operations of the routine  800  may be deployed on various other hardware configurations or be performed by similar components residing elsewhere. 
     In block  802 , the online parameter estimation component  402  detects an application requesting the visual inertial tracking system  108 . In block  804 , the online parameter estimation component  402  retrieves the latest estimated parameter value from the storage device  206 . In block  806 , the online parameter estimation component  402  calibrates the visual inertial tracking system  108  based on the latest estimated parameter value and factory calibration. 
       FIG.  9    is a flow diagram illustrating a routine  900  for updating visual odometry of an AR display device in accordance with one example embodiment. Operations in the routine  900  may be performed by the tracking calibration module  216 , using Components (e.g., modules, engines) described above with respect to  FIG.  3   . Accordingly, the routine  900  is described by way of example with reference to the tracking calibration module  216 . However, it shall be appreciated that at least some of the operations of the routine  900  may be deployed on various other hardware configurations or be performed by similar Components residing elsewhere. 
     In block  902 , the periodic parameter estimation component  404  detects a trigger event. In block  904 , the periodic parameter estimation component  404  starts the visual inertial tracking system  108  in response to the trigger event. In block  906 , the periodic parameter estimation component  404  determines a latest estimated parameter value. In block  908 , the periodic parameter estimation component  404  stores the latest estimated parameter value in the storage device  206 . In block  910 , the periodic parameter estimation component  404  turns off the visual inertial tracking system  108 . 
       FIG.  10    is a flow diagram illustrating a method for calibrating a visual-inertial tracking system in accordance with one embodiment. In block  1002 , routine  1000  operates, at a device, the visual-inertial tracking system without receiving a tracking request from a virtual object display application. In block  1004 , routine  1000  in response to operating the visual-inertial tracks system, accessing sensor data from a plurality of sensors of the device. In block  1006 , routine  1000  identifies, based on the sensor data, a first calibration parameter value of the visual-inertial tracking system. In block  1008 , routine  1000  stores the first calibration parameter value. In block  1010 , routine  1000  detects the tracking request from the virtual object display application to the visual-inertial tracking system. In block  1012 , routine  1000  in response to detecting the tracking request, accesses the first calibration parameter value and determining a second calibration parameter value from the first calibration parameter value. 
       FIG.  11    is a block diagram  1100  illustrating a software architecture  1104 , which can be installed on any one or more of the devices described herein. The software architecture  1104  is supported by hardware such as a machine  1102  that includes Processors  1120 , memory  1126 , and I/O Components  1138 . In this example, the software architecture  1104  can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture  1104  includes layers such as an operating system  1112 , libraries  1110 , frameworks  1108 , and applications  1106 . Operationally, the applications  1106  invoke API calls  1150  through the software stack and receive messages  1152  in response to the API calls  1150 . 
     The operating system  1112  manages hardware resources and provides common services. The operating system  1112  includes, for example, a kernel  1114 , services  1116 , and drivers  1122 . The kernel  1114  acts as an abstraction layer between the hardware and the other software layers. For example, the kernel  1114  provides memory management, Processor management (e.g., scheduling), Component management, networking, and security settings, among other functionalities. The services  1116  can provide other common services for the other software layers. The drivers  1122  are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  1122  can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth. 
     The libraries  1110  provide a low-level common infrastructure used by the applications  1106 . The libraries  1110  can include system libraries  1118  (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  1110  can include API libraries  1124  such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries  1110  can also include a wide variety of other libraries  1128  to provide many other APIs to the applications  1106 . 
     The frameworks  1108  provide a high-level common infrastructure that is used by the applications  1106 . For example, the frameworks  1108  provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks  1108  can provide a broad spectrum of other APIs that can be used by the applications  1106 , some of which may be specific to a particular operating system or platform. 
     In an example embodiment, the applications  1106  may include a home application  1136 , a contacts application  1130 , a browser application  1132 , a book reader application  1134 , a location application  1142 , a media application  1144 , a messaging application  1146 , a game application  1148 , and a broad assortment of other applications such as a third-party application  1140 . The applications  1106  are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications  1106 , structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application  1140  (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application  1140  can invoke the API calls  1150  provided by the operating system  1112  to facilitate functionality described herein. 
       FIG.  12    is a diagrammatic representation of the machine  1200  within which instructions  1208  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1200  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  1208  may cause the machine  1200  to execute any one or more of the methods described herein. The instructions  1208  transform the general, non-programmed machine  1200  into a particular machine  1200  programmed to carry out the described and illustrated functions in the manner described. The machine  1200  may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1200  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 machine  1200  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1208 , sequentially or otherwise, that specify actions to be taken by the machine  1200 . Further, while only a single machine  1200  is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions  1208  to perform any one or more of the methodologies discussed herein. 
     The machine  1200  may include Processors  1202 , memory  1204 , and I/O Components  1242 , which may be configured to communicate with each other via a bus  1244 . In an example embodiment, the Processors  1202  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another Processor, or any suitable combination thereof) may include, for example, a Processor  1206  and a Processor  1210  that execute the instructions  1208 . The term “Processor” is intended to include multi-core Processors that may comprise two or more independent Processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG.  12    shows multiple Processors  1202 , the machine  1200  may include a single Processor with a single core, a single Processor with multiple cores (e.g., a multi-core Processor), multiple Processors with a single core, multiple Processors with multiples cores, or any combination thereof. 
     The memory  1204  includes a main memory  1212 , a static memory  1214 , and a storage unit  1216 , both accessible to the Processors  1202  via the bus  1244 . The main memory  1204 , the static memory  1214 , and storage unit  1216  store the instructions  1208  embodying any one or more of the methodologies or functions described herein. The instructions  1208  may also reside, completely or partially, within the main memory  1212 , within the static memory  1214 , within machine-readable medium  1218  within the storage unit  1216 , within at least one of the Processors  1202  (e.g., within the Processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1200 . 
     The I/O Components  1242  may include a wide variety of Components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O Components  1242  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O Components  1242  may include many other Components that are not shown in  FIG.  12   . In various example embodiments, the I/O Components  1242  may include output Components  1228  and input Components  1230 . The output Components  1228  may include visual Components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic Components (e.g., speakers), haptic Components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input Components  1230  may include alphanumeric input Components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input Components), point-based input Components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input Components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input Components), audio input Components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O Components  1242  may include biometric Components  1232 , motion Components  1234 , environmental Components  1236 , or position Components  1238 , among a wide array of other Components. For example, the biometric Components  1232  include Components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion Components  1234  include acceleration sensor Components (e.g., accelerometer), gravitation sensor Components, rotation sensor Components (e.g., gyroscope), and so forth. The environmental Components  1236  include, for example, illumination sensor Components (e.g., photometer), temperature sensor Components (e.g., one or more thermometers that detect ambient temperature), humidity sensor Components, pressure sensor Components (e.g., barometer), acoustic sensor Components (e.g., one or more microphones that detect background noise), proximity sensor Components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other Components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position Components  1238  include location sensor Components (e.g., a GPS receiver Component), altitude sensor Components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor Components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O Components  1242  further include communication Components  1240  operable to couple the machine  1200  to a network  1220  or devices  1222  via a coupling  1224  and a coupling  1226 , respectively. For example, the communication Components  1240  may include a network interface Component or another suitable device to interface with the network  1220 . In further examples, the communication Components  1240  may include wired communication Components, wireless communication Components, cellular communication Components, Near Field Communication (NFC) Components, Bluetooth® Components (e.g., Bluetooth® Low Energy), WiFi® Components, and other communication Components to provide communication via other modalities. The devices  1222  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication Components  1240  may detect identifiers or include Components operable to detect identifiers. For example, the communication Components  1240  may include Radio Frequency Identification (RFID) tag reader Components, NFC smart tag detection Components, optical reader Components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection Components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication Components  1240 , such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     The various memories (e.g., memory  1204 , main memory  1212 , static memory  1214 , and/or memory of the Processors  1202 ) and/or storage unit  1216  may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions  1208 ), when executed by Processors  1202 , cause various operations to implement the disclosed embodiments. 
     The instructions  1208  may be transmitted or received over the network  1220 , using a transmission medium, via a network interface device (e.g., a network interface Component included in the communication Components  1240 ) and using any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1208  may be transmitted or received using a transmission medium via the coupling  1226  (e.g., a peer-to-peer coupling) to the devices  1222 . 
     Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the present disclosure. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description. 
     The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. 
     Examples 
     Example 1 is a method for calibrating a visual-inertial tracking system comprising: operating, at a device, the visual-inertial tracking system without receiving a tracking request from a virtual object display application; in response to operating the visual-inertial tracking system, accessing sensor data from a plurality of sensors of the device; identifying, based on the sensor data, a first calibration parameter value of the visual-inertial tracking system; storing the first calibration parameter value; detecting the tracking request from the virtual object display application to the visual-inertial tracking system; and in response to detecting the tracking request, accessing the first calibration parameter value and determining a second calibration parameter value from the first calibration parameter value. 
     Example 2 includes example 1, wherein operating the visual-inertial tracking system further comprises: periodically accessing the sensor data from the plurality of sensors, wherein the visual-inertial tracking system operates independently from the virtual object display application. 
     Example 3 includes example 1, further comprising: detecting a calibration trigger event at the device, wherein operating the visual-inertial tracking system is in response to detecting the calibration trigger event. 
     Example 4 includes example 3, wherein the trigger event comprises at least one of a temperature change exceeding a temperature threshold, an accelerometer sensor value exceeding an accelerometer threshold value, a battery level of the device exceeding a battery threshold value, a user-activation of the device, or a detection that the device is worn by a user. 
     Example 5 includes example 1, wherein identifying the first calibration parameter value is based on a convergence between a first virtual object data point location and a second virtual object data point location, the first virtual object data point location being determined based on the sensor data that are adjusted with the first parameter value, the second virtual object data point location being determined based on the sensor data that are adjusted with a default calibration parameter value of the device. 
     Example 6 includes example 1, further comprising: turning off the visual-inertial tracking system after storing the first calibration parameter value. 
     Example 7 includes example 1, further comprising: calibrating the visual-inertial tracking system with the first calibration parameter value before detecting the tracking request from the virtual object display application. 
     Example 8 includes example 1, further comprising: calibrating the visual-inertial tracking system with the second calibration parameter value after detecting the tracking request from the virtual object display application. 
     Example 9 includes example 1, wherein determining the second calibration parameter value further comprises: replacing a starting value comprising the default calibration value with the first calibration parameter value; and performing a calibration of the visual-inertial tracking system starting with the starting value. 
     Example 10 includes example 1, wherein storing the first calibration parameter value further comprises: storing the first calibration value in a storage device of the device or at a server. 
     Example 11 is a computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to perform operations comprising: operate, at a device, the visual-inertial tracking system without receiving a tracking request from a virtual object display application; in response to operating the visual-inertial track system, accessing sensor data from a plurality of sensors of the device; identify, based on the sensor data, a first calibration parameter value of the visual-inertial tracking system; store the first calibration parameter value; detect the tracking request from the virtual object display application to the visual-inertial tracking system; and in response to detecting the tracking request, access the first calibration parameter value and determining a second calibration parameter value from the first calibration parameter value. 
     Example 12 includes example 11, wherein operating the visual-inertial track system further comprises: periodically access the sensor data from the plurality of sensors, wherein the visual-inertial track system operates independently from the virtual object display application. 
     Example 13 includes example 11, wherein the instructions further configure the apparatus to: detect a calibration trigger event at the device, wherein operating the visual-inertial track system is in response to detecting the calibration trigger event. 
     Example 14 includes example 13, wherein the trigger event comprises at least one of a temperature change exceeding a temperature threshold, an accelerometer sensor value exceeding an accelerometer threshold value, a battery level of the device exceeding a battery threshold value, a user-activation of the device, or a detection that the device is worn by a user. 
     Example 15 includes example 11, wherein identifying the first calibration parameter value is based on a convergence between a first virtual object data point location and a second virtual object data point location, the first virtual object data point location being determined based on the sensor data that are adjusted with the first parameter value, the second virtual object data point location being determined based on the sensor data that are adjusted with a default calibration parameter value of the device. 
     Example 16 includes example 11, wherein the instructions further configure the apparatus to: turn off the visual-inertial tracking system after storing the first calibration parameter value. 
     Example 17 includes example 11, wherein the instructions further configure the apparatus to: calibrate the visual-inertial tracking system with the first calibration parameter value before detecting the tracking request from the virtual object display application. 
     Example 18 includes example 11, wherein the instructions further configure the apparatus to: calibrate the visual-inertial tracking system with the second calibration parameter value after detecting the tracking request from the virtual object display application. 
     Example 19 includes example 11, wherein determining the second calibration parameter value further comprises: replace a starting value comprising the default calibration value with the first calibration parameter value; and perform a calibration of the visual-inertial tracking system starting with the starting value. 
     Example 20 includes a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising: operate, at a device, a visual-inertial tracking system without receiving a tracking request from a virtual object display application; in response to operating the visual-inertial track system, accessing sensor data from a plurality of sensors of the device; identify, based on the sensor data, a first calibration parameter value of the visual-inertial tracking system; store the first calibration parameter value; detect the tracking request from the virtual object display application to the visual-inertial tracking system; and in response to detecting the tracking request, access the first calibration parameter value and determining a second calibration parameter value from the first calibration parameter value.