A 3D point cloud segmentation method based on multi-line lidar

Abstract

The invention discloses a multiline laser radar-based 3D point cloud segmentation method. The method comprises the following steps of: 1) scanning 3D point cloud data in a 360-degree range by utilizing multiline laser radar, establishing a Cartesian coordinate system OXYZ, converting the 3D point cloud data under the Cartesian coordinate system, and pre-processing the 3D point cloud data under the Cartesian coordinate system so as to determine a region of interest in the 3D point cloud data; 2) filtering suspended obstacle points in the region of interest by utilizing the statistic characteristics of adjacent points; 3) constructing a polar coordinates grid map, mapping the 3D point cloud data, the suspended obstacle points of which are filtered, into the polar coordinates grid map, and segmenting non-ground point cloud data from the 3D point cloud data in the polar coordinates grid map; and 4) voxelizing the non-ground point cloud data by utilizing an octree and carrying out clustering segmentation by adoption of an octree voxel grid-based region growing method. The method 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.

Description

technical field [0001] The invention relates to the technical field of radar point cloud data processing, in particular to a segmentation method based on multi-line laser radar 3D point cloud data. Background technique [0002] In recent years, since 3D laser sensors such as Velodyne can obtain accurate depth information and are not affected by complex environmental factors such as illumination and weather changes, they have been widely used in the fields of environment perception and 3D reconstruction of unmanned vehicles. The 3D point cloud data obtained by scanning the surrounding scene with a multi-line laser sensor such as Velodyne contains reflection data of almost all objects in the surrounding environment of the sensor. By correspondingly processing the scanned point cloud data, the purpose of detecting and identifying obstacles in the scanning scene can be achieved. [0003] Occasionally, due to the sensor itself, a small number of radar error reflection points are...

AI Technical Summary

Problems solved by technology

The advantage of the detection method based on obstacle grid is that it reduces three-dimensional information to two-dimensional information, greatly reduces the complexity and calculation amount of sensor data analysis, and has better stability and real-time performance. , which reduces false detection points, but due to the uneven distribution of radar point clouds, especially in the distance, the radar 3D point cloud is sparse, which may easily lead to missed detection of distant grids due to the lack of part of the point cloud

Based on the method of polar grid line fitting and surface fitting, although the influence of uneven distribution of radar point cloud is solved, because the fitting process requires continuous iteration, it affects real-time performance

The method based on the scan line gradient needs to establish complex neighborhood relationships and extract complex features in the point cloud segmentation, and the method based on the scan line gradient, in the vicinity, due to the high resolution of the radar and the dense point cloud, when When the points are relatively close, as long as the height difference is slightly raised, a larger gradient value may be obtained. Therefore, in the point cloud segmentation near the radar, it is possible to mistakenly detect small raised ground points as obstacle points

[0005] When clustering and segmenting non-ground point clouds, the most commonly used segmentation methods are clustering segmentation based on Euclidean distance, the method based on k-nearest neighbor region growth, and the method of using nearest neighbor search after grid projection, etc., based on Euclidean distance The method of distance and k-nearest neighbor region growth is low in complexity and easy to implement, but requires a neighbor search for each point. For depth sensors such as Velodye that can generate millions of point clouds per second, segmentation is difficult to meet real-time requirements ; For the segmentation method of grid projection, the non-ground points are projected onto the plane grid, and the grid is used as the clustering object to cluster through the eight-neighborhood search, which avoids clustering each point cloud. For the clustering of data with a large number of point clouds, the calculation speed is improved. However, when multiple obstacles overlap (such as a vehicle under a tree), the projection of the point cloud will superimpose the two obstacles and it is difficult to separate them.

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