基于改进DBSCAN的激光雷达障碍物检测

Obstacle Detection of Lidar Based on Improved DBSCAN Algorithm

为了解决激光雷达障碍物检测中点云密度不均和分割不彻底导致的误检、漏检和实时性差等问题,提出了一种改进的DBSCAN (Density-Based Spatial Clustering of Applications with Noise)算法以提高障碍物聚类效果。首先利用散乱点云数据建立k维树(kD tree)索引,在完成数据预处理的基础上,使用射线地面分离(RGF)算法进行地面分割。然后对传统的DBSCAN算法进行了改进,聚类半径随扫描距离的变化自适应地改变,远距离障碍物点云聚类效果得到提高。实验结果表明,所提方法对不同距离的障碍物都能实现良好的聚类,与传统方法相比,平均耗时减少了1.18 s,正检率提高了19.60个百分点。

In order to solve the problems of false detection, missing detection and poor real-time performance caused by uneven density and incomplete segmentation of point cloud in lidar obstacle detection, an improved DBSCAN(Density-Based Spatial Clustering of Applications with Noise) algorithm is proposed to improve the effect of obstacle clustering. Firstly, the k dimensional tree(kD tree) index is established with scattered point cloud data, and the RGF(Ray Ground Filter) algorithm is used to segment the ground points after the raw data is preprocessed. Then, the traditional DBSCAN algorithm is improved to change the clustering radius of obstacles adaptively with scanning distance, and the clustering effect of long-distance obstacle point clouds is improved. The experimental results show that the proposed method can achieve good clustering for obstacles with different distances, its average time consumption is reduced by 1.18 s and its positive detection rate is increased by 19.60 percentage points compared with those of the traditional method.