Wu Xia

TL;DR: In this article, a road boundary detection and tracking method is proposed, which consists of acquiring polar coordinate data of radar scanning points of the surroundings of an unmanned vehicle through radar scanning, and converting the polar coordinates data into rectangular coordinates; mapping radar points into a polar coordinate grid, and according to the height of extending vertexes in the grid, extracting ground points; extracting road boundary points through the radial distance of the scanning points from the road boundary under the same scanning line as well as the height mutation properties of the neighboring scanning points and by combining with a road extending direction.

TL;DR: In this paper, a ground point cloud segmentation method based on a three-dimensional laser radar is proposed, which comprises the steps of acquiring polar coordinate data of 3D laser radar scanning point cloud in the surrounding environment of a vehicle and converting the polar coordinates to a local rectangular coordinate system; correcting radar data by utilizing a vehicle-mounted IMU and an odometer; constructing a polar coordinate grid map, and extracting an extension vertex in each grid according to vertical continuity of point cloud distribution in the grids; extracting ground points in non-marginal grids according to height attributes of the

TL;DR: In this article, a multiline laser radar-based 3D point cloud segmentation method is proposed to determine a region of interest in the 3D Point Cloud data, which can improve the operation efficiency, is high in detection precision and strong in reliability, and can be widely applied to the technical field of vehicle environment perception.

TL;DR: In this article, a target detection method based on 3D laser radar and image data is proposed, which comprises the following steps: acquiring 3D point cloud data and camera images of the surrounding environment by using 3D LRS and camera, and pre-processing the 3D LRRS data; filtering out ground points in the point clouds data, performing a spatial clustering on the remaining non-ground points, and extracting the threeD interest region of the target; calibrating the external parameters of the coordinates of the 3d LRRS and the camera; and mapping the 3