一种面向复杂环境的自适应激光里程计设计

Design of an adaptive laser odometry for complex environments

针对在复杂环境下使用传统三维点云配准算法构建的激光里程计精度低且建图易发生漂移的问题,本文设计了一种面向复杂环境的自适应激光里程计。首先通过三维激光雷达采集原始点云数据,经过点云预处理环节后,采用地面分割方法完成点云数据分割并获取路面点云丰富度信息;然后,使用NDT算法将前后两帧点云数据极大限度的进行拉近,实现点云数据的粗配准;最后,在环境判断结论指引下选择合适的ICP算法完成三维点云的高精度配准并根据输出的点云变换关系构建激光里程计。通过在数据集以及不同环境下的大量实车测试,得出该激光里程计在室内结构化环境中的平均位移误差为0.026 m,在室外非结构化环境中的平均位移误差为0.1 m。结果表明,本文构建的激光里程计能够更好的适应复杂环境从而得到更加精确的三维点云地图与SLAM轨迹。

Aiming at the problems of low accuracy and easy drift of the laser odometry constructed by traditional 3D point cloud registration algorithm in complex environments,this paper proposes an adaptive laser odometry for complex environments.First,the original point cloud data was collected by 3D Lidar,and after the point cloud preprocessing,the ground segmentation method was used to complete the point cloud data segmentation and obtain the road point cloud richness information;then,the NDT algorithm was used to convert the front and rear the frame point cloud data is zoomed to the maximum extent to realize the rough registration of the point cloud data;finally,under the guidance of the environmental judgment conclusion,the appropriate ICP algorithm was selected to complete the highprecision registration of the 3D point cloud and according to the output point cloud transformation relationship built the laser odometry.Through the data set and a large number of real vehicle tests in different environments,it is concluded that the average displacement error of the laser odometry in the indoor structured environment is 0.026 m,and the average displacement error in the outdoor unstructured environment is 0.1 m.The results show that the laser odometry constructed in this paper can better adapt to complex environments and obtain more accurate 3D point cloud maps and SLAM trajectories.