基于改进欧氏聚类算法的障碍物检测跟踪

Obstacle Detection and Tracking Based on Improved Euclidean Clustering Algorithm

障碍物的检测与跟踪技术是移动机器人行驶过程中的一个重要技术,有利于提高移动机器人的运动安全.为了提高了障碍物检测的准确率,针对欧氏聚类存在过分割和欠分割的情况,做出了两点改进:提出动态欧氏聚类搜索半径的方法来解决远处点云过于稀疏的问题;提出将半径搜索改成深度方向上的拓展搜索的方法来解决点云数据在深度方向上检测不完全和拖尾等问题.为了提高动态障碍物跟踪的准确率,在进行两帧障碍物数据关联时,设计了一种新的关联矩阵的计算方式,加入了障碍物的六自由度信息和尺寸信息,提高了动态匹配的成功率.仿真实验表明,经过改进后障碍物检测准确率达到了95.2%,多目标跟踪精度达到了13.2 mm.

Obstacle detection and tracking technology is an important technology in the process of mobile robot driving,which is conducive to improving the movement safety of mobile robots.In order to improve the accuracy of obstacle detection,two improvements have been made to overcome the over-segmentation and under-segmentation of Euclidean clustering.A dynamic Euclidean clustering search radius method is proposed to solve the problem of too sparse distant point clouds,and a method of changing radius search to extended search in the depth direction is proposed to solve the problems of incomplete detection and trailing in the depth direction of point cloud data.In order to improve the accuracy of dynamic obstacle tracking,a new calculation formula of association matrix is designed when two frame obstacle data association is performed,and six degrees of freedom information and size information of the obstacle are added,which improves the success rate of dynamic matching.Simulation experiments show that the improved obstacle detection accuracy reaches 95.2%,and the multi-target tracking accuracy reaches 13.2 mm.