基于KDTree树和欧式聚类的越野环境下行人识别的研究

Research on Pedestrian Recognition in Cross-country Environment Based on KDTree and Euclidean Clustering

越野环境下行人的识别是班组伴随自动驾驶车辆的基础要求。本文中针对激光雷达点云数据中的行人识别问题,特别是越野环境下的特殊问题,提出基于聚类思想的解决方案。在理论分析的基础上,结合人的几何物理特征,设计了基于KDTree和欧式聚类的行人识别算法,并在越野环境下履带式车辆上进行试验。结果表明,所设计的激光雷达行人识别算法能准确识别激光雷达点云数据中的行人,在越野环境下有良好的识别率。

Pedestrian recognition in cross-country environment is a fundamental requirement of group accompanied automatic driving vehicle.Aiming at the problem of pedestrian recognition in lidar point cloud data,especially under cross-country environment,a clustering-based solution is proposed in this paper.On the basis of theoretical analysis,combined with the geometric and physical features of human being,a pedestrian recognition algorithm based on KDTree and Euclidean clustering is designed and a corresponding test is carried out on tracked vehicle under corss-country environment.The results show that the lidar-based pedestrian recognition algorithm designed can accurately identify the pedestrians in lidar point cloud data,with a good recognition rate in cross-country environment.