考虑反射强度的全自动运行列车障碍物检测算法研究

Aiming at the problem of obstacle detection in front of rail transit train, this paper proposes an obstacle detection algorithm based on LiDAR and considering laser reflection intensity for fully automatic train. This algorithm uses Euclidean clustering method to cluster the point clouds, and combines the adaptive threshold processing of reflection intensity, VGF, clustering radius differentiation and other methods to improve the speed and accuracy of obstacle detection. Experiments show that the algorithm has good detection performance.

针对轨道交通列车前方障碍物检测问题，本文提出一种适用于全自动列车的基于激光雷达并考虑激光反射强度的障碍物检测算法。该算法采用欧几里得聚类方法对点云进行聚类，并结合反射强度自适应阈值处理、体素网格滤波器（VGF）、聚类半径区分等方法来提高障碍物检测的速度和精度。实验表明该算法具有良好的检测性能。