Automated tagging for landmark identification

The different illustrative embodiments provide a method for identifying landmarks in an image. An image of a worksite is received. The image is analyzed to determine a suggested identity of a worksite feature in the image. The suggested identity of the worksite feature is sent over a communications unit. A confirmation of the suggested identity of the worksite feature is received to form a confirmed identity. The confirmed identity and a number of attributes associated with the confirmed identity is stored in a database.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods for navigation and more particularly to systems and methods for mobile robotic navigation. Still more specifically, the present disclosure relates to automated tagging for landmark identification.

BACKGROUND OF THE INVENTION

The use of robotic devices to perform physical tasks has increased in recent years. Mobile robotic devices can be used to perform a variety of different tasks. These mobile devices may operate in semi-autonomous or fully autonomous modes. These robotic devices may have an integrated navigation system for performing a variety of different tasks in semi-autonomous or fully autonomous modes. Mobile robotic devices often rely on visual landmarks and physical perimeters for localization and navigation. Visual landmarks used for localization and navigation in triangulation require persistent landmarks that are identified and localized. These persistent landmarks may be precisely surveyed with global positioning systems or manual measurement methods, which is time-consuming and may be cost-prohibitive. Mobile robotic devices may also be used to survey visual landmarks, but lack the capability to identify and distinguish persistence of a landmark or position stability of the landmark.

SUMMARY

The different illustrative embodiments provide a method for identifying landmarks in an image. An image of a worksite is received. The image is analyzed to determine a suggested identity of a worksite feature in the image. The suggested identity of the worksite feature is sent over a communications unit. A confirmation of the suggested identity of the worksite feature is received to form a confirmed identity. The confirmed identity and a number of attributes associated with the confirmed identity is stored in a database.

The different illustrative embodiments further provide a system for identifying landmarks in an image comprising a worksite features database, a user interface, and a data processing system. The data processing system is configured to execute a data classification process to analyze an image, determine a suggested identity of a worksite feature in the image using the worksite features database, send the suggested identity of the worksite feature over a communications unit, receive a confirmation of the suggested identity of the worksite feature to form a confirmed identity, and store the confirmed identity and a number of attributes associated with the confirmed identity in the worksite features database.

The features, functions, and advantages can be achieved independently in various embodiments of the present invention, or may be combined in yet other embodiments, in which further details can be seen with reference to the following description and drawings.

DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference to the figures and in particular with reference toFIG. 1, a block diagram of a worksite environment is depicted in which an illustrative embodiment may be implemented. Worksite environment100may be any type of worksite environment in which an autonomous vehicle can operate. In an illustrative example, worksite environment100may be a structure, building, worksite, area, yard, golf course, indoor environment, outdoor environment, different area, change in the needs of a user, and/or any other suitable worksite environment or combination of worksite environments.

As an illustrative example, a change in the needs of a user may include, without limitation, a user moving from an old location to a new location and operating an autonomous vehicle in the yard of the new location, which is different than the yard of the old location. As another illustrative example, a different area may include, without limitation, operating an autonomous vehicle in both an indoor environment and an outdoor environment, or operating an autonomous vehicle in a front yard and a back yard, for example.

Worksite environment100includes network101in one embodiment of the present invention. In this example, back office102may be a single computer or a distributed computing cloud. Back office102supports the physical databases and/or connections to external databases which may be used in the different illustrative embodiments. Back office102may supply databases to different vehicles, as well as provide online access to information from databases. Back office102may also provide path plans for vehicles, such as autonomous vehicle104, for example.

Back office102includes worksite feature identification system103. Worksite feature identification system103automatically identifies worksite features detected by autonomous vehicle104in number of worksites106.

Worksite environment100may include autonomous vehicle104, number of worksites106, user108, and manual control device110. Autonomous vehicle104may be any type of autonomous vehicle including, without limitation, a mobile robotic machine, a service robot, a field robot, a robotic mower, a robotic snow removal machine, a robotic leaf removal machine, a robotic lawn watering machine, a robotic vacuum, and/or any other autonomous vehicle. Autonomous vehicle104includes navigation system112.

Navigation system112provides a system for controlling the mobility, positioning, and navigation for autonomous vehicle104. System capabilities may include base behaviors such as, for example, without limitation, base mobility functions for effectuating random area coverage of a worksite, base obstacle avoidance functions for contact switch obstacle avoidance, base dead reckoning for positioning functions, and/or any other combination of basic functionality for autonomous vehicle104. Navigation system112includes environmental data collection system114used for detecting environmental data within a worksite, such as number of worksites106.

Environmental data detected by environmental data collection system114may be sent to worksite feature identification system103over network101for use in landmark identification and localization. Navigation system112may also include path planning capabilities for navigating autonomous vehicle104within number of worksites106to perform area coverage tasks, for example. Number of worksites106may be any area within worksite environment100in which autonomous vehicle104can operate. Each worksite in number of worksites106may be associated with a number of tasks. Worksite116is an illustrative example of one worksite in number of worksites106. For example, in an illustrative embodiment, worksite116may be a back yard of a residence of a user. Worksite116includes number of environmental data collection locations117and number of tasks118. Number of environmental data collection locations117is any location where environmental data collection system114may detect environmental data within number of worksites106, for example. In an illustrative example, number of tasks118may include mowing the back yard of the residence of a user. Autonomous vehicle104may operate to perform number of tasks118within worksite116. As used herein, number refers to one or more items. In one illustrative example, number of worksites106may include, without limitation, a primary yard and a secondary yard. The primary yard may be worksite116, associated with number of tasks118. The secondary yard may be associated with another set of tasks, for example.

In one illustrative example, environmental data collection system114may be moved to number of environmental data collection locations117within worksite116by autonomous vehicle104. In another illustrative example, environmental data collection system114may be moved to number of environmental data collection locations117by a human, an animal, a human controlled vehicle, and/or any other suitable mobile platform.

Each worksite in number of worksites106may include a number of worksite features. Worksite116includes number of worksite features120. Number of worksite features120may be any type of visual feature of worksite116, including, without limitation, landmarks, vegetation, structures, wildlife, buildings, and/or any other suitable visual feature. Number of worksite features120may include number of landmarks122and number of other features124.

Number of landmarks122may be any type of landmark capable of being detected by autonomous vehicle104. In an illustrative example, number of landmarks122may include, without limitation, cylindrical landmarks, colored landmarks, patterned landmarks, illuminated landmarks, vertical landmarks, natural landmarks, any combination of the foregoing, and/or any other suitable landmark. Patterned landmarks may include a visual pattern incorporated to provide distinctive information, for example. Illuminated landmarks may provide visual detection in low-light or no-light situations, such as night time, for example. Natural landmarks may include, for example, without limitation, tree trunks. Other types of landmarks may include, for example, building architectural features, driveways, sidewalks, curbs, fences, and/or any other suitable landmarks.

Number of other features124is any type of visual feature of worksite116not suitable as a landmark. Number of other features124may include, for example, without limitation, ground cover, flowering plants, and/or any other type of non-persistent object. A non-persistent object may be for example, without limitation, patio furniture.

User108may be, without limitation, a human operator, a robotic operator, or some other external system. Manual control device110may be any type of manual controller, which allows user108to override autonomous behaviors and control autonomous vehicle104and/or worksite feature identification system103. Manual control device110may also provide a user interface for user108to provide input to worksite feature identification system103, such as confirmation of automatic identification, for example. In an illustrative example, user108may use manual control device110to control movement of autonomous vehicle104within worksite116in order to perform number of tasks118and/or identify number of worksite features120. In another illustrative example, user108may use manual control device110to confirm automatic identification of number of worksite features120by worksite feature identification system103.

For example, in one illustrative embodiment, worksite feature identification system103may be located on autonomous vehicle104. In this illustrative example, worksite feature identification system103may dynamically identify worksite features detected by autonomous vehicle104in number of worksites106.

The different illustrative embodiments recognize and take into account that currently used methods for robotic navigation often use a very primitive, random navigation system. This random navigation system works within a perimeter established by a wire carrying an electrical signal. The robotic machines in currently used methods may be equipped with an electrical signal detector and a bumper switch on the body of the machine. These machines move in a generally straight direction until they either detect the signal from the perimeter wire, or a bumper switch is closed due to contact of the machine with an external object. When either of these two situations occur, these machines change direction. In this way, current methods constrain the machine within a work area perimeter and maintain movement after contact with external objects.

The different illustrative embodiments further recognize and take into account that currently used visual navigation systems for robotic navigation require persistent landmarks to be identified and localized within a worksite. These persistent landmarks may currently be precisely surveyed with global positioning systems or manual measurement methods, which are time-consuming and cost-prohibitive. Further, current mobile robotic devices used to survey visual landmarks lack the capability to identify and distinguish persistence of a landmark or position stability of the landmark.

Thus, one or more of the different illustrative embodiments provide a method for identifying landmarks in an image. An image of a worksite is received. The image is analyzed to determine a suggested identity of a worksite feature in the image. The suggested identity of the worksite feature is sent over a communications unit. A confirmation of the suggested identity of the worksite feature is received to form a confirmed identity. The confirmed identity and a number of attributes associated with the confirmed identity is stored in a database.

The different illustrative embodiments further provide a system for identifying landmarks in an image comprising a worksite features database, a user interface, and a data processing system. The data processing system is configured to execute a data classification process to analyze an image, determine a suggested identity of a worksite feature in the image using the worksite features database, send the suggested identity of the worksite feature over a communications unit, receive a confirmation of the suggested identity of the worksite feature to form a confirmed identity, and store the confirmed identity and a number of attributes associated with the confirmed identity in the worksite features database.

The different illustrative embodiments provide a system for accurate landmark identification and localization through automated landmark detection and tagging.

With reference now toFIG. 2, a block diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system200is an example of a computer, such as back office102inFIG. 1, in which computer usable program code or instructions implementing the processes may be located for the illustrative embodiments.

In this illustrative example, data processing system200includes communications fabric202, which provides communications between processor unit204, memory206, persistent storage208, communications unit210, input/output (I/O) unit212, and display214.

Input/output unit212allows for input and output of data with other devices that may be connected to data processing system200. For example, input/output unit212may provide a connection for user input through a keyboard, a mouse, and/or some other suitable input device. Further, input/output unit212may send output to a printer. Display214provides a mechanism to display information to a user.

These instructions are referred to as program code, computer usable program code, or computer readable program code that may be read and executed by a processor in processor unit204. The program code in the different embodiments may be embodied on different physical or tangible computer readable media, such as memory206or persistent storage208.

Program code218is located in a functional form on computer readable media220that is selectively removable and may be loaded onto or transferred to data processing system200for execution by processor unit204. Program code218and computer readable media220form computer program product222in these examples. In one example, computer readable media220may be in a tangible form, such as, for example, an optical or magnetic disc that is inserted or placed into a drive or other device that is part of persistent storage208for transfer onto a storage device, such as a hard drive that is part of persistent storage208. In a tangible form, computer readable media220also may take the form of a persistent storage, such as a hard drive, a thumb drive, or a flash memory that is connected to data processing system200. The tangible form of computer readable media220is also referred to as computer recordable storage media. In some instances, computer readable media220may not be removable.

As another example, a storage device in data processing system200is any hardware apparatus that may store data. Memory206, persistent storage208and computer readable media220are examples of storage devices in a tangible form.

FIG. 3is a block diagram of a navigation system in accordance with an illustrative embodiment. Navigation system300is an example of one implementation of navigation system112inFIG. 1.

Navigation system300includes processor unit302, communications unit304, behavior database306, worksite database308, mobility system310, sensor system312, power supply314, power level indicator316, base system interface318, and environmental data collection system320. Environmental data collection system320includes environmental sensor system322, plurality of databases324, and environmental data generator326.

Environmental sensor system322detects worksite features, such as number of worksite features120inFIG. 1, and other environmental data in a worksite, such as number of worksites106inFIG. 1. Other environmental data may include, for example, without limitation, wind, precipitation, temperature, season, and/or any other suitable environmental data. Environmental sensor system322includes vision system328. Vision system328may be, for example, without limitation, a stereo vision system, an asymmetric vision system, a stadiametric ranging vision system, and/or any other suitable vision system. Vision system328includes number of cameras330. Number of cameras330may be used to capture images of a worksite or worksite area, such as worksite116inFIG. 1, for example. The images captured by number of cameras330may be transferred over base system interface318to processor unit302for use in landmark identification and path planning, for example. As used herein, “number of” refers to one or more images. Number of cameras330may include, for example, without limitation, a color camera, a black and white camera, a digital camera, an infrared camera, and/or any other suitable camera.

Plurality of databases324of environmental data collection system320provide horticultural and weather information associated with a worksite environment, such as worksite environment100inFIG. 1. Plurality of databases324may include, for example, without limitation, horticultural database332, weather database334, on-line database336, and/or any other suitable database.

Horticultural database332may include information such as, without limitation, plant species and varieties, information about the water needs, growth stages, characteristics, and life cycles of the plant species and varieties, specific environmental features of a worksite environment that may affect autonomous vehicles, and the like. For example, characteristics of various plant species may be, without limitation, trunk, bark, branching system, stem size, leaf pattern, budding, non-budding, color, growth pattern, preferred sunlight, preferred soil moisture, preferred soil pH, and the like. Weather database334may include current weather for a worksite environment, weather history for a worksite environment, and the like.

Online database336may use communications unit304to wirelessly access the Internet. Online database336dynamically provides information to environmental data collection system320, which enables adjustment to data generated by environmental data generator326. For example, online database336may include, without limitation, current weather conditions of the worksite environment, current constraints for the worksite environment, weather forecasts for the worksite environment, and/or any other suitable information. Current constraints may include a number of constraints, such as, without limitation, ground conditions undesirable for an autonomous vehicle, such as flooding, for example.

In some examples, online database336may be a remotely accessed database. This weather information provided by online database336may be used by environmental data collection system320to determine which sensors of environmental sensor system322to activate in order to acquire accurate environmental data for the worksite environment. Weather, such as rain, snow, fog, and frost may limit the range of certain sensors, and require an adjustment in attributes of other sensors, in order to acquire accurate environmental data from the worksite environment. Other types of information that may be obtained include, without limitation, vegetation information, such as foliage deployment, leaf drop status, and lawn moisture stress.

Environmental data generator326uses images captured by number of cameras330, other environmental data detected by environmental sensor system322, and information from plurality of databases324to generate worksite data327about a worksite, such as worksite116inFIG. 1. Worksite data327may be sent to worksite feature identification system103inFIG. 1for use in automated tagging of worksite features on a worksite map, for example.

Processor unit302may be an example of one implementation of data processing system200inFIG. 2. Processor unit302includes vehicle control process338. Vehicle control process338is configured to communicate with and control mobility system310. Vehicle control process338includes path planning module340. Path planning module340may use information from behavior database306and worksite database308, along with worksite data327received from environmental data collection system320, to generate path plan342. A path may be any length, for example, one foot or ten feet, and may change as the position of the autonomous vehicle relative to a landmark, obstacle, perimeter, and/or boundary changes.

In one illustrative example, vehicle control process338may retrieve a worksite map from worksite database308in order to path plan342across a worksite, such as worksite116inFIG. 1. Vehicle control process338may use path plan342to send commands and/or signals to mobility system310in order to move an autonomous vehicle associated with navigation system300according to path plan342. Vehicle control process338may move an autonomous vehicle across path plan342in order to survey a worksite and capture images for use in worksite feature identification, for example. Vehicle control process338may initiate the worksite survey in response to a trigger, such as, for example, without limitation, a button being selected on an autonomous vehicle, a command from a manual control device, a software-driven event, a time-driven event, and/or any other suitable trigger.

Processor unit302may further communicate with and access data stored in behavior database306and worksite database308. Accessing data may include any process for storing, retrieving, and/or acting on data in behavior database306and/or worksite database308. For example, accessing data may include, without limitation, using a lookup table housed in behavior database306and/or worksite database308, running a query process using behavior database306and/or worksite database308, and/or any other suitable process for accessing data stored in a database.

Processor unit302receives information from sensor system312and may use sensor information in conjunction with behavior data from behavior database306when controlling mobility system310. Processor unit302may also receive control signals from an outside controller, such as manual control device110operated by user108inFIG. 1, for example. These control signals may be received by processor unit302using communications unit304.

Communications unit304may provide communications links to processor unit302to receive information. This information includes, for example, data, commands, and/or instructions. Communications unit304may take various forms. For example, communications unit304may include a wireless communications system, such as a cellular phone system, a Wi-Fi wireless system, or some other suitable wireless communications system.

Communications unit304may also include a wired connection to an optional manual controller, such as manual control device110inFIG. 1, for example. Further, communications unit304also may include a communications port, such as, for example, a universal serial bus port, a serial interface, a parallel port interface, a network interface, or some other suitable port to provide a physical communications link. Communications unit304may be used to communicate with an external control device or user, for example.

In one illustrative example, processor unit302may receive control signals from manual control device110operated by user108inFIG. 1. These control signals may override autonomous behaviors of vehicle control process338and allow user108to stop, start, steer, and/or otherwise control the autonomous vehicle associated with navigation system300.

Behavior database306contains a number of behavioral actions which vehicle control process338may utilize when controlling mobility system310. Behavior database306may include, without limitation, basic vehicle behaviors, area coverage behaviors, perimeter behaviors, obstacle avoidance behaviors, manual control behaviors, power supply behaviors, and/or any other suitable behaviors for an autonomous vehicle.

Mobility system310provides mobility for an autonomous vehicle, such as autonomous vehicle104inFIG. 1. Mobility system310may take various forms. Mobility system310may include, for example, without limitation, a propulsion system, steering system, braking system, and mobility components. In these examples, mobility system310may receive commands from vehicle control process338and move an associated autonomous vehicle in response to those commands.

Sensor system312may include a number of sensor systems for collecting and transmitting sensor data to processor unit302. For example, sensor system312may include, without limitation, a dead reckoning system, an obstacle detection system, a perimeter detection system, and/or some other suitable type of sensor system, as shown in more illustrative detail inFIG. 5. Sensor data is information collected by sensor system312.

Power supply314provides power to components of navigation system300and the associated autonomous vehicle, such as autonomous vehicle104inFIG. 1, for example. Power supply314may include, without limitation, a battery, mobile battery recharger, ultracapacitor, fuel cell, gas powered generator, photo cells, and/or any other suitable power source. Power level indicator316monitors the level of power supply314and communicates the power supply level to processor unit302. In an illustrative example, power level indicator316may send information about a low level of power in power supply314. Processor unit302may access behavior database306to employ a behavioral action in response to the indication of a low power level, in this illustrative example. For example, without limitation, a behavioral action may be to cease operation of a task and seek a recharging station in response to the detection of a low power level.

Base system interface318provides power and data communications between environmental data collection system320and the other components of navigation system300. In an illustrative example, images captured by number of cameras330may be transferred to processor unit302from environmental data collection system320using base system interface318.

For example, in an illustrative embodiment, environmental sensor system322may be integrated with sensor system312of navigation system300. In another illustrative example, environmental data collection system320may be integrated with processor unit302of navigation system300.

In yet another illustrative embodiment, vision system328may be a separate component from environmental sensor system322, and interact with processor unit302and/or environmental data collection system320using base system interface318, for example. In yet another illustrative example, plurality of databases324may be located remotely from navigation system300and accessed by environmental data collection system320using communications unit304.

FIG. 4is a block diagram of a mobility system in accordance with an illustrative embodiment. Mobility system400is an example of one implementation of mobility system310inFIG. 3.

Mobility system400provides mobility for autonomous vehicles associated with a navigation system, such as navigation system300inFIG. 3. Mobility system400may take various forms. Mobility system400may include, for example, without limitation, propulsion system402, steering system404, braking system406, and number of mobility components408. In these examples, propulsion system402may propel or move an autonomous vehicle, such as autonomous vehicle104inFIG. 1, in response to commands from a navigation system, such as navigation system300inFIG. 3.

Propulsion system402may maintain or increase the speed at which an autonomous vehicle moves in response to instructions received from a processor unit of a navigation system. Propulsion system402may be an electrically controlled propulsion system. Propulsion system402may be, for example, without limitation, an internal combustion engine, an internal combustion engine/electric hybrid system, an electric engine, or some other suitable propulsion system. In an illustrative example, propulsion system402may include wheel drive motors410. Wheel drive motors410may be an electric motor incorporated into a mobility component, such as a wheel, that drives the mobility component directly. In one illustrative embodiment, steering may be accomplished by differentially controlling wheel drive motors410.

Steering system404controls the direction or steering of an autonomous vehicle in response to commands received from a processor unit of a navigation system. Steering system404may be, for example, without limitation, an electrically controlled hydraulic steering system, an electrically driven rack and pinion steering system, a differential steering system, or some other suitable steering system. In an illustrative example, steering system404may include a dedicated wheel configured to control number of mobility components408.

Braking system406may slow down and/or stop an autonomous vehicle in response to commands received from a processor unit of a navigation system. Braking system406may be an electrically controlled braking system. This braking system may be, for example, without limitation, a hydraulic braking system, a friction braking system, a regenerative braking system using wheel drive motors410, or some other suitable braking system that may be electrically controlled. In one illustrative embodiment, a navigation system may receive commands from an external controller, such as manual control device110inFIG. 1, to activate an emergency stop. The navigation system may send commands to mobility system400to control braking system406to perform the emergency stop, in this illustrative example.

Number of mobility components408provides autonomous vehicles with the capability to move in a number of directions and/or locations in response to instructions received from a processor unit of a navigation system and executed by propulsion system402, steering system404, and braking system406. Number of mobility components408may be, for example, without limitation, wheels, tracks, feet, rotors, propellers, wings, and/or other suitable components.

FIG. 5is a block diagram of a sensor system in accordance with an illustrative embodiment. Sensor system500is an example of one implementation of sensor system312inFIG. 3.

Sensor system500includes a number of sensor systems for collecting and transmitting sensor data to a processor unit of a navigation system, such as navigation system300inFIG. 3. Sensor system500includes obstacle detection system502, perimeter detection system504, and dead reckoning system506.

Obstacle detection system502may include, without limitation, number of contact switches508and ultrasonic transducer510. Number of contact switches508detects contact by an autonomous vehicle with an external object in the environment, such as worksite environment100inFIG. 1, for example. Number of contact switches508may include, for example, without limitation, bumper switches. Ultrasonic transducer510generates high frequency sound waves and evaluates the echo received back. Ultrasonic transducer510calculates the time interval between sending the signal, or high frequency sound waves, and receiving the echo to determine the distance to an object.

Perimeter detection system504detects a perimeter or boundary of a worksite, such as worksite116inFIG. 1, and sends information about the perimeter detection to a processor unit of a navigation system. Perimeter detection system504may include, without limitation, receiver512and infrared detector514. Receiver512detects electrical signals, which may be emitted by a wire delineating the perimeter of a worksite, such as worksite116inFIG. 1, for example. Infrared detector514detects infrared light, which may be emitted by an infrared light source along the perimeter of a worksite, such as worksite116inFIG. 1, for example.

In an illustrative example, receiver512may detect an electrical signal from a perimeter wire, and send information about that detected signal to a processor unit of a navigation system, such as navigation system300inFIG. 3. The navigation system may then send commands to a mobility system, such as mobility system400inFIG. 4, to alter the direction or course of an autonomous vehicle associated with the navigation system, in this illustrative example.

Dead reckoning system506estimates the current position of an autonomous vehicle associated with the navigation system. Dead reckoning system506estimates the current position based on a previously determined position, and information about the known or estimated speed over elapsed time and course. Dead reckoning system506may include, without limitation, odometer516, compass518, and accelerometer520. Odometer516is an electronic or mechanical device used to indicate distance traveled by a machine, such as autonomous vehicle104inFIG. 1. Compass518is a device used to determine position or direction relative to the earth's magnetic poles. Accelerometer520measures the acceleration it experiences relative to freefall.

FIG. 6is a block diagram of a behavior database in accordance with an illustrative embodiment. Behavior database600is an example of one implementation of behavior database306inFIG. 3.

Behavior database600includes a number of behavioral actions which vehicle control process338of navigation system300may utilize when controlling mobility system310inFIG. 3. Behavior database600may include, without limitation, basic vehicle behaviors602, area coverage behaviors604, perimeter behaviors606, obstacle avoidance behaviors608, manual control behaviors610, power supply behaviors612, and/or any other suitable behaviors for an autonomous vehicle.

Basic vehicle behaviors602provide actions for a number of basic tasks an autonomous vehicle may perform. Basic vehicle behaviors602may include, without limitation, mowing, vacuuming, floor scrubbing, leaf removal, snow removal, watering, spraying, security, and/or any other suitable task.

Area coverage behaviors604provide actions for area coverage when performing basic vehicle behaviors602. Area coverage behaviors604may include, without limitation, sector decomposition behaviors, boustrouphadon area coverage behaviors, spiral area coverage behaviors, customized path plans, and/or any other suitable area coverage behaviors. Sector decomposition behaviors may include, for example, without limitation, follow arc behavior, point-to-point behavior, and/or any other suitable behaviors.

Perimeter behaviors606provide actions for a navigation system in response to perimeter detection, such as by perimeter detection system504inFIG. 5. In an illustrative example, perimeter behaviors606may include, without limitation, follow perimeter, change heading, and/or any other suitable behaviors. Change heading may operate to change the heading for an autonomous vehicle by a number of degrees in order to stay within a perimeter, for example. Follow perimeter may operate to move an autonomous vehicle parallel to a perimeter for a predefined distance, for example. A predefined distance may be, for example, a distance equal to the width of the autonomous vehicle less an error amount.

Obstacle avoidance behaviors608provide actions for a navigation system to avoid collision with objects in an environment around an autonomous vehicle. In an illustrative example, obstacle avoidance behaviors608may include, without limitation, circle obstacle, reverse direction and change heading, and/or any other suitable behaviors. Circle obstacle may operate to direct an autonomous vehicle around an obstacle to continue along an original path, or around the entirety of an obstacle in order to perform a task on all areas around the obstacle, for example. Reverse direction and change heading may operate to reverse direction and change heading of an autonomous vehicle by a number of degrees before moving forward in order to avoid collision with an object detected by an obstacle detection system, such as obstacle detection system502inFIG. 5.

Manual control behaviors610provide actions for a navigation system to disable autonomy and take motion control from a user, such as user108inFIG. 1, for example. Power supply behaviors612provide actions for a navigation system to take a number of actions in response to a detected level of power in a power supply, such as power supply314inFIG. 3. In an illustrative example, power supply behaviors612may include, without limitation, stopping the task operation of an autonomous vehicle and seeking out additional power or power recharge for the autonomous vehicle.

FIG. 7is a block diagram of a worksite database in accordance with an illustrative embodiment. Worksite database700is an example of one implementation of worksite database308inFIG. 3.

Worksite database700includes a number of databases processor unit302of navigation system300may utilize when planning a path and/or controlling mobility system310inFIG. 3. Worksite database700may include, without limitation, map database702, landmark database704, and/or any other suitable database of information for an autonomous vehicle.

Map database702includes number of worksite maps706. Number of worksite maps706may correspond to number of worksites106inFIG. 1, for example. In one illustrative embodiment, number of worksite maps706may be loaded into map database702from a remote location, such as back office102inFIG. 1using network101. In another illustrative embodiment, number of worksite maps706may be stored in map database702after being updated by worksite feature identification system103inFIG. 1. In yet another illustrative embodiment, number of worksite maps706may be loaded into map database702by a user, such as user108inFIG. 1, over base system interface318and/or communications unit304inFIG. 3, for example.

Landmark database704may include landmark images and definitions708and position information710. Landmark images and definitions708may identify a number of landmarks in a worksite, such as number of landmarks122in worksite116inFIG. 1, for example. Position information710identifies the position of each landmark identified in landmark images and definitions708relative to locations within the worksite. Position information710may be associated with number of worksite maps706stored in map database702, for example.

In an illustrative example, landmark database704may be automatically updated by worksite feature identification system103using environmental data detected by environmental data collection system114inFIG. 1. In another illustrative example, landmark database704may be updated by a user, such as user108inFIG. 1, after receiving worksite feature data from environmental data collection system114inFIG. 1.

With reference now toFIG. 8, a block diagram of an environmental sensor system is depicted in accordance with an illustrative embodiment. Environmental sensor system800is an example of one implementation of environmental sensor system322of environmental data collection system320inFIG. 3.

As illustrated, environmental sensor system800may include, for example, without limitation, global positioning system802, structured light sensor804, two dimensional/three dimensional lidar806, dead reckoning808, radar810, ultrasonic sonar812, radio frequency identification reader (RFID)814, rain sensor816, ambient light sensor818, and vision system820. These different sensors may be used to identify the environment around an autonomous vehicle, such as autonomous vehicle104inFIG. 1. For example, these sensors may be used to detect worksite features in the worksite environment around an autonomous vehicle, such as number of worksite features120in worksite116. In another example, these sensors may be used to detect a dynamic condition in the environment. A dynamic condition may be, for example, without limitation, wind speed, wind direction, precipitation, temperature, and/or any other condition in a worksite environment.

Global positioning system802may identify the location of the autonomous vehicle with respect to other objects in the worksite environment. Global positioning system802may be any type of radio frequency triangulation scheme based on signal strength and/or time of flight. Examples include, without limitation, the Global Positioning System, Glonass, Galileo, and cell phone tower relative signal strength. Position is typically reported as latitude and longitude with an error that depends on factors, such as ionospheric conditions, satellite constellation, and signal attenuation from vegetation. Localization information detected by global positioning system802may be used to identify position and localization of a landmark within a worksite environment, for example.

Structured light sensor804emits light in a pattern, such as one or more lines, reads back the reflections of light through a camera, and interprets the reflections to detect and measure objects in the environment. Two dimensional/three dimensional lidar806is an optical remote sensing technology that measures properties of scattered light to find range and/or other information of a distant target. Two dimensional/three dimensional lidar806emits laser pulses as a beam, then scans the beam to generate two dimensional or three dimensional range matrices. The range matrices are used to determine distance to an object or surface by measuring the time delay between transmission of a pulse and detection of the reflected signal.

Dead reckoning808begins with a known position, which is then advanced, mathematically or directly, based upon known speed, elapsed time, and course. The advancement based upon speed may use the vehicle odometer, or ground speed radar, to determine distance traveled from the known position. Radar810is well known in the art, and may be used in a time of flight mode to calculate distance to an object, as well as Doppler mode to calculate the speed of an object. Radar810uses electromagnetic waves to identify the range, altitude, direction, or speed of both moving and fixed objects. Ultrasonic sonar812uses sound propagation on an ultrasonic frequency to measure the distance to an object by measuring the time from transmission of a pulse to reception and converting the measurement into a range using the known speed of sound. Ultrasonic sonar812is well known in the art and can also be used in a time of flight mode or Doppler mode, similar to radar810. Radio frequency identification reader (RFID)814relies on stored data and remotely retrieves the data using devices called radio frequency identification (RFID) tags or transponders. Rain sensor816detects precipitation on an exterior surface of the autonomous vehicle. Ambient light sensor818measures the amount of ambient light in the worksite environment.

Vision system820may be, for example, without limitation, a stereo vision system, an asymmetric vision system, a stadiametric ranging vision system, and/or any other suitable vision system. Vision system820includes number of cameras822. Number of cameras822may be any type of camera, including, without limitation, an infrared camera, visible light camera, a color camera, a black and white camera, a digital camera, and/or any other suitable camera. An infrared camera detects heat indicative of a living thing versus an inanimate object. An infrared camera may also form an image using infrared radiation. A visible light camera may be a standard still-image camera, which may be used alone for color information or with a second camera to generate stereoscopic or three-dimensional images. When a visible light camera is used along with a second camera to generate stereoscopic images, the two or more cameras may be set with different exposure settings to provide improved performance over a range of lighting conditions. A visible light camera may also be a video camera that captures and records moving images. Number of cameras822capture number of images824. Number of images824may be used by environmental data collection system320inFIG. 3to generate environmental data about a worksite. Number of images824may also be used by worksite feature identification system103inFIG. 1to automatically identify worksite features detected in a worksite by environmental sensor system800.

Environmental sensor system800may retrieve environmental data from one or more of the sensors to obtain different perspectives of the environment. For example, environmental sensor system800may obtain visual data from vision system820, data about the distance of the vehicle in relation to objects in the environment from two dimensional/three dimensional lidar806, and location data of the vehicle in relation to a map from global positioning system802.

With reference now toFIG. 9, a block diagram of a worksite feature identification system is depicted in accordance with an illustrative embodiment. Worksite feature identification system900is an illustrative example of one implementation of worksite feature identification system103inFIG. 1.

Worksite feature identification system900includes worksite features database902, data classification process904, and user interface906. Worksite features database902includes number of candidate objects908, object properties910, number of identified objects912, number of confirmed objects914, number of worksite maps916, and number of images917. Number of candidate objects908may be a list of objects which have positional stability and/or are suitable as landmarks for use in localization and navigation, for example. Number of candidate objects908may include, for example, without limitation, concrete slab, flower bed, fence, fence post, flag pole, tree trunk, utility pole, outbuilding, house, garden structure, and/or any other suitable object with positional stability.

Number of candidate objects908may also include object attributes907associated with each of the candidate objects in number of candidate objects908. Object attributes907is information about characteristics of candidate objects, such as, without limitation, color, texture, shape, ratio of dimension, and/or any other suitable characteristic. Each object in number of candidate objects908may be associated with one or more attributes in object attributes907, in this example. Information about color in object attributes907may provide a color space to represent points in a RGB color model, such as hue, saturation, and value (HSV) color space or hue, saturation, and lightness (HSL) color space, for example. Information about texture in object attributes907may be presented with a numeric value associated with a scale of textures from rough to smooth, for example. Information about shape in object attributes907may include references such as, convex, concave, flat, multifaceted, and/or any other suitable shape reference, for example. Information about ratio of height in object attributes907may be presented with a numeric value associated with a ratio of height to width, for example.

Object properties910includes a list of properties associated with number of candidate objects908. Object properties910may include, for example, without limitation, traversability by an autonomous vehicle, non-traversability by an autonomous vehicle, seasonal change, material components, and/or any other suitable object property. Object properties910are attributes of number of candidate objects908that have a real world effect on the physical behavior of an autonomous vehicle, such as autonomous vehicle104inFIG. 1, for example. In one illustrative example, number of candidate objects908may include a concrete slab associated with an object property of traversability by an autonomous vehicle, resulting in a physical behavior of an autonomous vehicle in traversing the concrete slab while performing a number of tasks in a worksite. In another illustrative example, number of candidate objects908may include a flower bed associated with an object property of non-traversability by an autonomous vehicle, resulting in a physical behavior of an autonomous vehicle avoiding and/or going around the flower bed while performing a number of tasks in a worksite. In yet another illustrative example, object properties910may be associated with operational instructions for an autonomous vehicle. In this example, number of candidate objects908may include a tree associated with an object property of non-traversability and operational instructions of “do not bump.”

Object properties910associated with worksite features identified in a worksite map may also be used by a navigation system, such as navigation system300, in planning autonomous vehicle behaviors and path plans within a worksite. The real world behavior of an autonomous vehicle is impacted by object properties910associated with number of candidate objects908identified in a worksite environment.

Number of identified objects912may include object properties, description, and/or images associated with an object identification. Number of identified objects912represents worksite features automatically identified by data classification process904. In one illustrative example, number of identified objects912may include an image of a cylindrical, narrow, vertical structure, having object attributes of the color brown and texture rough, and object properties of non-traversability. In this example, the object may be identified as “tree trunk” by data classification process904.

Number of confirmed objects914may include object properties, description, and/or images associated with an object identification that has been confirmed by a user, such as user108inFIG. 1, for example. In an illustrative example, data classification process904may automatically identify an image of a cylindrical, narrow, vertical structure, having object attributes of the color brown and texture rough, and object properties of non-traversability, as “tree trunk.” A user may provide user input918over user interface906to confirm this automatic identification, and data classification process904may store the confirmed object identification and properties in number of confirmed objects914. Number of confirmed objects914may also include additional information added by a user via user input918, such as the diameter or size of an object, for example. This additional information may be used by data classification process904to generate a scaled worksite map, for example.

Number of worksite maps916are generated by data classification process904and stored in worksite features database902for use in path planning by a navigation system of an autonomous vehicle, such as navigation system300inFIG. 3, for example.

Number of images917may be previously generated image data of a worksite, stored images of a worksite, images of a worksite downloaded from a back office or user, and/or any other suitable image. Number of images917may be used by image analysis process922to analyze image data924and identify worksite features, in one illustrative example.

Data classification process904includes image analysis process922. Data classification process904may receive image data924and/or environmental data926from environmental data collection system320inFIG. 3. Image data924may be a number of images transmitted directly from vision system328inFIG. 3, for example. Environmental data926may include images and associated environmental data for a worksite environment generated by environmental data generator326inFIG. 3, for example.

Image analysis process922analyzes image data924and/or environmental data926and uses worksite features database902to generate number of object identifications928. Image analysis process922may also compare image data924and/or environmental data926to number of images917in worksite features database902to identify common features between at least two images in number of images917.

Data classification process904includes image sequencer930. Image sequencer930analyzes multiple images of a worksite received from image data924and/or environmental data926to generate compiled image931. Compiled image931is a three dimensional model of the multiple images of the worksite with point clouds of photographed objects in the images. Image sequencer930may use pattern recognition algorithms, which are well known in the art, to compare portions of images to create points. Image sequencer930then compares these points to convert the image into a model. Image sequencer930extracts number of three dimensional models932out of compiled image931. Number of three dimensional models932is a three dimensional model of a number of objects in the images. In one illustrative example, image sequencer930may extract a three dimensional model of a central object, such as a tree, from compiled image931.

Image analysis process922identifies object attributes934of number of three dimensional models932. Object attributes934are physical characteristics of an object such as, for example, without limitation, color, texture, shape, ratio of dimension, and/or any other suitable characteristic. Image analysis process922uses information in number of candidate objects908, such as object attributes907associated with candidate objects, to generate number of object identifications928. Image analysis process922may traverse number of candidate objects908and select a candidate object based on the likelihood of match using object attributes934identified for the number of three dimensional models932.

In an illustrative example, if number of three dimensional models932has object attributes934of narrow, vertical, cylindrical, brown, and rough texture, image analysis process922may identify a candidate object in number of candidate objects908that has the highest number of shared attributes from the selection of objects in number of candidate objects908. In this illustrative example, candidate object pole936may be associated with object attributes907of narrow, vertical, cylindrical, and brown, while candidate object tree trunk938may be associated with object attributes907of narrow, vertical, cylindrical, brown, and rough texture. In this illustrative example, candidate object tree trunk938has a higher number of shared attributes between object attributes934and object attributes907and is selected by image analysis process922as the object identification based on the highest likelihood of a match.

Image data924may include scaling information940associated with worksite features in an image. In an illustrative example, scaling information940may be information about an object in relation to an autonomous vehicle at the time the image was captured by a vision system of the autonomous vehicle, for example. In another illustrative example, scaling information940may be information about an object in relation to the position of a camera at the time of capturing the image, where the camera is operated by a human, for example. Scaling information940is used to determine the scale, or size, of an object in an image. For example, scaling information940may include information on the position of the autonomous vehicle when the image was captured, the vantage point of the number of cameras of a vision system of the autonomous vehicle, the scan angle of a lidar sensor of the autonomous vehicle, the distance of the object from the autonomous vehicle, and/or any other suitable information. In the illustrative example of a tree trunk, the diameter of the tree trunk may be calculated by image analysis process922using scaling information940to generate geometries, for example.

Data classification process904generates number of worksite maps916stored in worksite features database902. Number of worksite maps916are tagged worksite maps having a number of identified objects associated with object properties that affect the real world, physical operation of an autonomous vehicle in a worksite. Number of worksite maps916is used for localization and path planning by a navigation system, such as navigation system300, for example.

Data classification process904may optionally send number of object identifications928to a user over user interface906for confirmation of the automated identification of worksite features in an image or environmental data, for example. The user may confirm the automated identification and/or identify additional properties or operational instructions. Confirmed identifications may be stored in number of confirmed objects914, for example.

With reference now toFIG. 10, a block diagram of an image is depicted in accordance with an illustrative embodiment. Image1000is an illustrative example of one implementation of number of images824inFIG. 8and/or compiled image931inFIG. 9.

In one illustrative example, image1000may be compiled by an image sequencer, such as image sequencer930inFIG. 9, using a number of images captured by a vision system, such as vision system328inFIG. 3, for example. Image sequencer930may extract three dimensional models from image1000, such as number of three dimensional models932inFIG. 9. In this illustrative example, a central object of image1000is object A1002. Object A1002may be extracted by image sequencer930and outlined in red on image1000when displayed using user interface906inFIG. 9for confirmation. Image analysis process922identifies object attributes for object A1002. In this example, the object attributes for object A1002may be narrow, vertical, cylindrical, brown, and rough texture. Key1001provides identification of colors used to outline extracted three-dimensional features.

Image analysis process922identifies, or tags, object A1002as tree1004in image1000. The identification may be determined using a worksite features database, such as worksite features database902inFIG. 9. For example, image analysis process922may traverse number of candidate objects908to identify the nearest match of characteristics between the candidate objects and object A1002. When object A1002is identified as tree1004, object properties associated with a tree are then associated with object A1002. Image analysis process922may identify associated properties in number of object properties910or worksite features database902, for example. Object properties associated with a tree may be, for example, non-traversability or operational instructions of “do not bump.”

Image sequencer930may extract object B1006in image1000and outline object B1006in yellow when displayed using user interface906inFIG. 9for confirmation, for example. Image analysis process922identifies, or tags, object B1006as grass1008using worksite features database902inFIG. 9. Grass1008may be associated with object properties such as traversability, for example. In this illustrative example, object B1006tagged as grass1008and associated with the property of traversability affects the physical operation of an autonomous vehicle in the worksite by providing for the autonomous vehicle to traverse grass1008when executing tasks within the worksite, for example.

Image sequencer930may extract object C1010in image1000and outline object C1010in white when displayed using user interface906inFIG. 9for confirmation. Image sequencer930may also identify a number of other objects with similar three dimensional characteristics in image1000and outline the number of other objects in white. Image analysis process922may identify object C1010as a fence post, and propagate that identification to the number of other objects extracted with the same characteristics, tagging fence posts1012, for example.

With reference now toFIG. 11, a flowchart illustrating a process for identifying landmarks in an image is depicted in accordance with an illustrative embodiment. The process inFIG. 11may be implemented by a component such as worksite feature identification system103inFIG. 1, for example.

The process begins by receiving an image of a worksite (step1102). The image may be captured by an environmental data collection system, such as environmental data collection system114inFIG. 1, for example. The image may include a number of worksite features, such as landmarks, objects, and other features present in a worksite.

The process analyzes the image to determine a suggested identity of an object in the image (step1104). The process may analyze the image using an image analysis process of a data classification process, such as data classification process904inFIG. 9, for example. The process then sends the suggested identity of the object over a communications unit (step1106). The suggested identity may be sent to a user via a user interface, such as user interface906inFIG. 9, for example. A user may receive the suggested identity and confirm or change the suggested identity through user input918inFIG. 9, for example.

The process next determines whether the suggested identity is confirmed (step1108). If a determination is made that the suggested identity is confirmed, the process stores the confirmed identity with a number of attributes associated with the confirmed identity in a database (step1110), with the process terminating thereafter.

If a determination is made that the suggested identity is not confirmed, the process then determines whether an existing identity class is selected instead of the suggested identity (step1112). An existing identity class may be any object identification stored and/or previously created within a worksite feature identification system, such as worksite feature identification system900inFIG. 9. An identity class may refer to any object identification, such as number of candidate objects908inFIG. 9, for example.

If a determination is made that an existing identity class is selected, the process proceeds to step1110, with the process terminating thereafter. If a determination is made that an existing identity class is not selected, the process then determines whether a new identity class is created (step1114). A new identity class may be any object identification not currently stored and/or recognized within a worksite feature identification system, such as worksite feature identification system900inFIG. 9. The new identity class may be created and/or stored in a database, such as number of candidate objects908inFIG. 9, for example.

If a determination is made that a new identity class is not created, the process terminates. If a determination is made that a new identity class is created, the process proceeds to step1110, with the process terminating thereafter.

The process may store the confirmed identity in a database, such as number of confirmed objects914inFIG. 9, for example. The confirmed identity may be used to identify a landmark of a worksite in a worksite map, for example.

With reference now toFIG. 12, a flowchart illustrating a process for identifying worksite features is depicted in accordance with an illustrative embodiment. The process inFIG. 12may be implemented by a component such as worksite feature identification system103inFIG. 1, for example.

The process begins by obtaining a number of worksite images (step1202). The number of worksite images may be obtained using a vision system, such as vision system328inFIG. 3, for example. An image sequencer, such as image sequencer930inFIG. 9, may compile the number of worksite images into a compiled image, or a three dimensional model of the worksite, for example. The process identifies a common feature in at least two of the number of worksite images (step1204). The common feature may be an extracted three dimensional object from the three dimensional model of the worksite generated from the number of worksite images, for example.

The process then identifies a relative location of the common feature identified in the at least two images (step1206). The relative location may be a location relative to the worksite, for example. The relative location may be determined based on analysis of the number of worksite images, such as stereographic analysis, for example. In another illustrative example, the relative location of the common feature may be based on the compilation of the number of worksite images, and the location of the common feature in the three dimensional model generated from the number of worksite images.

The process then determines whether there are any remaining common features in the number of worksite images (step1208). If a determination is made that there are remaining common features in the number of worksite images, the process returns to step1204. If a determination is made that there are no remaining common features in the number of worksite images, the process then determines whether scaling information is available (step1210).

If a determination is made that scaling information is not available, the process terminates. If a determination is made that scaling information is available, the process applies the scaling information to a worksite map to provide distance information for the worksite and a size of the common feature identified (step1212), with the process terminating thereafter.