动态环境下无人地面车辆点云地图快速重定位方法

Rapid Localization of Unmanned Ground Vehicles in Dynamic Environment Using Point Cloud Maps

无人地面车辆在战时全球定位系统定位信号缺失情况下,依靠车载传感器确定在已有地图中的位置是无人车辆发挥效能的关键。现有方法多基于二维栅格地图,难以适用于场景变化大的复杂环境,为此提出了一种动态环境下基于三维点云地图、采用聚类词带模型的快速定位算法。通过连续多帧激光雷达点云数据进行反向溯源,利用贝叶斯公式进行动态障碍物剔除,对点云进行聚类并提取各类的视点特征直方图描述子,进而构建词典与数据库并快速匹配,实现无人车点云地图中快速起始位置寻址;基于改进的实时激光定位与建图算法,完成了后续精准定位。实车实验结果表明:该算法在动态环境下能有效准确地在三维点云地图中精准定位,满足实时性需求。

The use of on-board sensors to determine the position of unmanned ground vehicle in a map without global positioning system(GPS)signals is the key to realize the autonomous driving.The existing methods are mainly based on 2-D grid maps,and are not suitable for complex environment.A fast localization algorithm,which is based on 3D point cloud map in dynamic environment and uses a clustered bag-of-words model,is proposed.The dynamic obstacles are removed by backtracking the continuous multi-frame lidar point cloud data and using Bayesian formula.The word list and database are constructed by clustering the point clouds and extracting the viewpoint feature histogram(VFH)descriptors,which enables the unmanned ground vehicle to quickly find the starting position in the point cloud map.Then the subsequent precise positioning is achieved by using the lidar odometry and mapping(LOAM)real-time algorithm.The experimental results show that the proposed algorithm can be used to accurately localize an unmanned ground vehicle in the 3D point cloud map in dynamic environment and meet the real-time requirements.