An object of the present invention is to provide an object sensing device that classifies an observation point group output by a LiDAR into a real image and a mirror image when a road surface around an own vehicle is wet, and can use the mirror image for detecting the real image. An object sensing device that detects an object around a vehicle based on a point cloud data of an observation point observed by a LiDAR mounted on the vehicle includes: a road surface shape estimation unit that estimates a shape of a road surface; a road surface condition estimation unit that estimates a dry/wet situation of the road surface; and an observation point determination unit that determines a low observation point observed at a position lower than the estimated road surface by a predetermined amount or more when the road surface is estimated to be in a wet situation. The object is detected by using point cloud data of the observation points other than the low observation point and point cloud data of an inverted observation point obtained by inverting the low observation point with reference to a height of the road surface.

Systems, methods, and computer-readable media are provided for detecting a pavement marking around an autonomous vehicle, comparing the detected pavement marking with a pavement marking present in a semantic data map, determining whether a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map, and updating the semantic data map based on the determining of whether the change has occurred between the detected pavement marking and the pavement marking present in the semantic data map.

Systems and methods for operating a robotic system. The methods comprise: inferring, by a computing device, a first heading distribution for the object from a 3D point cloud; obtaining, by the computing device, a second heading distribution from a vector map; obtaining, by the computing device, a posterior distribution of a heading using the first and second heading distributions; defining, by the computing device, a cuboid on a 3D graph using the posterior distribution; and using the cuboid to facilitate driving-related operations of a robotic system.

In one embodiment, a lidar system includes a light source, a receiver, and a controller. The light source is configured to emit an optical signal. The receiver is configured to detect a received optical signal that includes a portion of the emitted optical signal that is scattered by a surface of a target located a distance from the lidar system, where the surface is oriented at an angle of incidence with respect to the emitted optical signal. The receiver is further configured to produce an electrical signal corresponding to the received optical signal. The controller is configured to determine, based on the electrical signal, the angle of incidence of the surface of the target.

Depth-sensing apparatus includes a laser, which emits pulses of optical radiation toward a scene. One or more detectors receive the optical radiation that is reflected from points in the scene and to output signals indicative of respective times of arrival of the received radiation. A scanner scans the pulses of optical radiation across the scene along successive parallel scan lines of a raster. Control and processing circuitry drives the laser to emit a succession of output sequences of the pulses with different, respective temporal spacings between the pulses within the output sequences in the succession, and matches the times of arrival of the signals due to the optical radiation reflected from the points in at least two adjacent scan lines in the raster to the temporal spacings of the output sequences in order to find respective times of flight for the points in the scene.

Systems and methods are disclosed that provide contextual tracking information to tracking sensor systems to provide accurate and efficient object tracking. Contextual data of a first tracking sensor system is used to identify a tracked object of a second tracking sensor system.

Systems and methods are provided for processing lidar data. The lidar data can be obtained in a particular manner that allows reconstruction of rectilinear images for which image processing can be applied from image to image. For instance, kernel-based image processing techniques can be used. Such processing techniques can use neighboring lidar and/or associated color pixels to adjust various values associated with the lidar signals. Such image processing of lidar and color pixels can be performed by dedicated circuitry, which may be on a same integrated circuit. Further, lidar pixels can be correlated to each other. For instance, classification techniques can identify lidar and/or associated color pixels as corresponding to the same object. The classification can be performed by an artificial intelligence (AI) coprocessor. Image processing techniques and classification techniques can be combined into a single system.

Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

One object determining system comprising: an air ejection device, configured to eject air; a distance detecting circuit, configured to detect distances between an electronic device comprising the object determining system and at least one location of an object when the air ejection device ejects air to the object; and a determining circuit, configured to determine a type of the object according to variations of the distances.

This disclosure provides a method and apparatus for recognizing a parking space.

An aspect of the present disclosure provides a method, performed by an apparatus of a host vehicle, for recognizing a parking space, the method including: recognizing another vehicle and a pillar based on data acquired from a light detection and ranging (lidar) device; recognizing a parking slot marking based on an image captured by a camera; and generating a map of an indoor parking lot based on information on the another vehicle, the pillar, and the parking slot marking.