A position recognizing device according to one embodiments of the present disclosure includes a ranging point acquiring section, a region determining section, and a ranging point excluding section. The ranging point acquiring section is configured to acquire ranging point information in which distances to ranging points are associated with each of electromagnetic wave applying directions. The region determining section is configured to determine whether an object region that represents a region encompassed by joining ranging points that are in close proximity to one another exists at a position closer than a specific ranging point representing a certain ranging point among the ranging points. The ranging point excluding section is configured to define the ranging point in front of which the object region exists as a false image point at which no object actually exists and exclude the false image point from the ranging point information.

This document describes techniques and systems for generating a fused object bounding box based on uncertainty. At least two bounding boxes, each associated with a different sensor, is generated. A fused center point and yaw angle as well as length, width, and velocity can be found by mixing the distributions of the parameters from each bounding box. A discrepancy between the center points of each bounding box can be used to determine whether to refine the fused bounding box (e.g., find an intersection between at least two bounding boxes) or consolidate the fused bounding box (e.g., find a union between at least two bounding boxes). This results in the fused bounding box having a confidence level of the uncertainty associated with the fused bounding box. In this manner, better estimations of the uncertainty of the fused bounding box may be achieved to improve tracking performance of a sensor fusion system.

According to at least some example embodiments, a computer-readable medium stores computer-executable program instructions that, when executed by a processor, cause the processor to perform operations including, obtaining plane information of a plane by using first distances from a terminal to a plurality of points on the plane; obtaining a normal vector of the plane by using direction information of the terminal measured by a direction sensor and the plane information; determining, based on the normal vector, a parameter of an object to be displayed on the plane; and displaying, on a display of the terminal, the object according to the determined parameter.

The present application provides a position detecting method, device and storage medium for a vehicle ladar, where the method includes: detecting, through a ladar disposed on an autonomous vehicle, detection data of at least one wall of an interior room in which the autonomous vehicle is located, obtaining a point cloud image according to the detection data of the at least one wall, and judging, according to the point cloud image, whether an installation position of the ladar is accurate. According to the technical solution, it is possible to accurately detect whether the installation position of the ladar is accurate, provide a prerequisite for calibration of the installation position of the ladar, and improve detection accuracy of the ladar for obstacles around the autonomous vehicle.

The present disclosure provides a laser projection component and a detection method thereof, and an electronic device. The laser projection component is applicable to the depth camera component and is configured to project a laser pattern. The detection method includes: obtaining the laser pattern; determining whether a preset identifier exists in the laser pattern; and determining that the depth camera component is abnormal when the preset identifier does not exist in the laser pattern.

A method and system for automatically processing point cloud based on reinforcement learning are provided. The method for automatically processing point cloud based on reinforcement learning according to an embodiment of the present disclosure includes scanning to collect a point cloud (PCL) and an image through a lidar and a camera; calibrating, by a controller, to match locations of the image and the point cloud through reinforcement learning that maximizes a reward including geometric and luminous intensity consistency of the image and the point cloud; and meshing, by the controller, the point cloud into a 3D image through reinforcement learning that minimizes a reward including a difference between a shape of the image and a shape of the point cloud.

A method, apparatus, and system for estimating a moving speed of a detected pedestrian at an autonomous driving vehicle (ADV) is disclosed. A pedestrian is detected in a plurality of frames of point clouds generated by a LIDAR device installed at an autonomous driving vehicle (ADV). In each of at least two of the plurality of frames of point clouds, a minimum bounding box enclosing points corresponding to the pedestrian excluding points corresponding to limbs of the pedestrian is generated. A moving speed of the pedestrian is estimated based at least in part on the minimum bounding boxes across the at least two of the plurality of frames of point clouds. A trajectory for the ADV is planned based at least on the moving speed of the pedestrian. Thereafter, control signals are generated to drive the ADV based on the planned trajectory.

Aspects and implementations of the present disclosure relate to filtering of return points from a point cloud based on radial velocity measurements. An example method includes: receiving, by a sensing system of an autonomous vehicle (AV), data representative of a point cloud comprising a plurality of return points, each return point comprising a radial velocity value and position coordinates representative of a reflecting region that reflects a transmission signal emitted by the sensing system; applying, to each of the plurality of return points, at least one threshold condition related to the radial velocity value of a given return point to identify a subset of return points within the plurality of return points; removing the subset of return points from the point cloud to generate a filtered point cloud; and identifying objects represented by the remaining return points in the filtered point cloud.

This application discloses a compensation method and apparatus for continuous wave ranging and a LiDAR. The compensation method includes: calculating a reflectivity of an object detected by a receiving unit, querying, based on a preset mapping relation, for a target distance response non-uniformity (DRNU) calibration compensation matrix associated with the reflectivity, and compensating, using the target DRNU calibration compensation matrix, for a distance of the object detected by the receiving unit.

An information processing method includes a detection step including detecting a target based on a distance image of a monitoring region; and a stay decision step including making a stay decision. The stay decision includes determining whether any stay of the target has occurred. The stay decision step includes making the decision based on an index indicating a positional change of the target with the passage of time.