The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

There is provided a mobile robot that performs the obstacle avoidance, positioning and object recognition according to image frames captured by the same optical sensor. The mobile robot includes an optical sensor, a light emitting diode, a laser diode and a processor. The processor identifies an obstacle and a distance thereof according to image frames captured by the optical sensor when the laser diode is emitting light. The processor further performs the positioning and object recognition according to image frames captured by the optical sensor when the light emitting diode is emitting light.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may determine a first real-world geo-spatial location of the treatment system. The system can receive captured images depicting real-world agricultural objects of a geographic scene. The system can associate captured images with the determined geo-spatial location of the treatment system. The treatment system can identify, from a group of mapped and indexed images, images having a second real-word geo-spatial location that is proximate with the first real-world geo-spatial location. The treatment system can compare at least a portion of the identified images with at least a portion of the captured images. The treatment system can determine a target object and emit a fluid projectile at the target object using a treatment device.

The invention relates to a control and navigation device for an autonomously moving system, which comprises the following: a sensor device, which is configured to acquire sensor data, and for this purpose a LiDAR sensor installation, which is configured for 360-degree acquisition; a fisheye camera installation, which is configured for 360-degree acquisition; and a radar sensor installation, which is configured for 360-degree acquisition; a data processing installation with an AI-based software application, which is configured to determine control signals for purposes of navigating an autonomously moving system by means of processing of the sensor data; and a data communication interface, which is connected to the data processing installation, and is configured to provide the control signals for transmission to a controller of the autonomously moving system. The sensor device, the data processing installation, and the data communication interface are arranged at an assembly component, which is configured to assemble, in a detachable manner, the sensor device, the data processing installation, and the data communication interface together as a common module at the autonomously moving system. Furthermore, an autonomously moving system is provided.

Systems and methods for tracking an object. The method comprising: receiving, by a processor, a series of observations made over time for the object; selecting, by the processor, a plurality of sets of observations using the series of observations; causing, by the processor, the plurality of sets of observations to be used by at least one filter to generate a track for the object (wherein the at least one filter uses sensor data associated with each of a plurality of frames of sensor data only once during generation of the track); and causing, by the processor, operations of an autonomous robot to be controlled based on the track for the object.

A method includes identifying sensor data associated with corresponding distal ends of one or more axles of an autonomous vehicle (AV). The method further includes determining, based on the sensor data, mass distribution data of the AV. The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes causing, based on the mass distribution data, performance of a corrective action associated with the AV.

A modular control system for controlling an AGV includes an interface, a processor, a memory, and a plurality of programs. The plurality of programs include a task scheduling module, a sensor fusion module, a mapping module, and a localization module. The interface receives a command signal from an AGV management system and sensor signals from a plurality of sensors. The memory stores a surrounding map and the plurality of programs to be executed by the processor. The task scheduling module converts the command signal to generate an enabling signal. The sensor fusion module processes the received sensor signals according to the enabling signal and generates an organized sensor data. The mapping module processes the organized sensor data and the surrounding map to generate an updated surrounding map. The localization module processes the organized sensor data and the updated surrounding map to generate a location and pose signal.

A method for operating an automated vehicle includes controlling by one or more computing devices an autonomous vehicle; receiving by one or more computing devices sensor data from the vehicle corresponding to moving objects in a vicinity of the vehicle; receiving by one or more computing devices road condition data; and determining by one or more computing devices undesirable locations related to the moving objects. The undesirable locations related to the moving objects for the vehicle are based at least in part on the road condition data. The step of controlling the vehicle includes avoiding the undesirable locations.

A system includes a tracking system, a controller-circuit, and a device. The tracking system is configured to detect and track an object, and includes one or more of a computer vision system, a radar system, and a LIDAR system. The controller-circuit is disposed in a host vehicle, and is configured to receive detection signals from the tracking system, process the detection signals, determine, whether an object is detected based on the processed detecting signals, and in accordance with a determination that an object is detected, output command signals. The device is adapted to be mounted to the host vehicle, and is configured to receive the command signals and thereby provide a dynamic visual indication adapted to change in accordance with orientation changes between the host vehicle and the object. The dynamic visual indication is viewable from outside of the host vehicle.

An information processing device (100) according to an embodiment includes a reception unit (121) that accepts designation of a tracking target, a setting unit (127) that sets a virtual tracking target different from the tracking target, and a drive control unit (129) that controls driving of a mobile body on the basis of the virtual tracking target.