基于欧氏聚类的改进激光雷达障碍物检测方法

Improved Lidar Obstacle Detection Method Based on Euclidean Clustering

在激光雷达检测障碍物过程中,由于点云近密远疏的特性,车辆的变速移动使得对物体进行分割时出现点云漂移和距离较近的物体难以被分割等现象,容易产生漏检或误检。为了解决此问题,提出一种基于点云射线角度约束的改进欧氏聚类算法,使障碍物检测更加快速准确,所提算法有效解决了点云密度不均匀导致的检测障碍物成功率较低的问题,同时对所提算法进行实车实验。实验结果表明,与传统欧氏聚类算法相比,所提算法能快速准确地对一定范围内的障碍物进行分割和聚类。

During lidar detection of obstacles,owing to the characteristics of near dense and far sparse point clouds,the movement with variable speeds of vehicles results in point cloud drifting in the object segmentation.Moreover,objects close to each other are difficult to be segmented,resulting in omissions or incorrect detections.To address these problems,this study proposes an improved Euclidean clustering algorithm based on the point cloud shot-line angle constraint to make obstacle detection more rapid and accurate.The proposed algorithm effectively solves the problem of low success rate in detecting obstacles owing to the uneven point cloud density.Simultaneously,experiments are performed on the proposed algorithm.The experimental results show that the proposed algorithm can quickly and accurately segment and cluster obstacles within a certain range compared with the traditional Euclidean clustering algorithm.