Road edge detection system and method based on laser radar and fan-shaped space segmentation

Abstract

The invention relates to a road edge detection system and method based on laser radar and fan-shaped space segmentation. The method comprises the steps of 1, a laser radar scanning the surrounding environment of a vehicle to obtain reflection point cloud information and convert the reflection point cloud information into a locally constructed three-dimensional coordinate system; 2, preprocessing the point cloud data, and separating and extracting ground data in each frame of point cloud; 3, dividing the space in the coordinate system into fan-shaped structural bodies according to the data characteristics of the laser radar and the point cloud, and identifying the road extension direction according to the ground information and the fan-shaped structural bodies; 4, extracting road edge candidate points in the point cloud by using a parallel road edge retrieval algorithm; 5, clustering the road edge candidate points, and eliminating an interference point set according to fan-shaped spatial features; and 6, performing B spline curve fitting on the finally determined road edge point to obtain a road edge detection result. The method is high in adaptability, capable of adapting to roadsof various shapes and reducing the influence of obstacles, high in precision and reduction degree, high in reliability and low in error rate.

Description

technical field [0001] The invention belongs to the technical field of automatic driving, and more specifically relates to a roadside detection system and method based on lidar and fan-shaped space segmentation. Background technique [0002] With the development of unmanned driving industry technology, the perception algorithm of lidar has become a research hotspot. As a sensor for unmanned vehicles, lidar has the advantages of high data dimension, accurate depth information, fast response frequency and high detection accuracy. [0003] In the prior art, a method of using gradient filtering to obtain roadside candidate points has been proposed, but this method is not effective in dealing with obstacles blocking the roadside, roadside shape changes, and slope changes, because the scanning of multi-line radar emission lines The radii are not the same, and the characteristics of the roadside reflection points at different distances from the car are also different. Therefore, r...

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Problems solved by technology

[0003] In the prior art, a method of using gradient filtering to obtain roadside candidate points has been proposed, but this method is not effective in dealing with obstacles blocking the roadside, roadside shape changes, and slope changes, because the scanning of multi-line radar emission lines The radii are not the same, and the characteristics of the roadside reflection points at different distances from the car are also different. Therefore, re-scanning the roadside, analyzing the results of obstacle recognition, and clustering are more stable choices; This method is used to segment point clouds and extract roadside point sets according to the distance from the vehicle. However, this method only finds out the gradient change points closest to the vehicle, and the clustering features are single. Large data, poor robustness; there is also a method of extracting candidate roadside feature points using a distance-based algorithm based on the linear characteristics of the roadside, but the extraction results are easily affected by obstacles on the road, that is, it is difficult to distinguish Identify whether the identified roadside points belong to obstacles or roadsides, because obstacles can also be regarded as a short-distance "roadside", and obstacles on the road often block the detection of the lidar on the roadside, The detection can only be completed under idealized conditions; while the roadside detection research compared in the past two years has a grid-based method, which reduces the dimensionality of the point cloud to a two-dimensional grid network and obtains the pixel value of the grid unit pixel. information, but this method reduces the dimensional richness of the point cloud, and the square grid network destroys the data characteristics of the lidar, which is easy to cause artificial features

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