面向变化场景的LiDAR鲁棒定位与地图维护方法

LiDAR robust positioning and map maintenance method for changing scenes

无人车在工业场景自主运行时,通常基于先验地图匹配对自身进行定位,然而场景变化会影响其定位精度。针对于此,本文提出了一种面向变化场景的激光雷达鲁棒定位与地图维护架构,其包括地图匹配、定位优化、地图维护模块。在该架构中,提出了一种基于变化检测的匹配算法,降低了变化场景引起的匹配误差;设计了基于激光雷达里程计与先验地图匹配的因子图融合模式,提高了定位解算鲁棒性;提出了一种基于最近点搜索的误检测点滤波方法,提高了变化点检测准确性。最后,通过仿真与试验搭建了变化场景验证环境,对基于Loam的匹配算法与本文算法进行了对比验证。结果表明,本文算法可以有效抑制场景变化引起的匹配误差,在实际场景中定位均方根误差优于3 cm,定位精度相较于传统算法提高67.4%。

Unmanned vehicles usually locate themselves based on prior map matching when operating autonomously in industrial scenarios. However, the scene change will affect the positioning accuracy of unmanned vehicles. In view of this, we propose a LiDAR robust positioning and map maintenance architecture for changing scenarios, which includes map matching, positioning optimization, and map maintenance modules. A matching algorithm based on change detection is proposed, which reduces the matching error caused by changing scenarios. A factor graph fusion mode based on LiDAR odometer and prior map matching is designed to improve the robustness of the positioning solution. A filtering method of false detection points based on nearest point search is proposed, which improves the accuracy of change point detection. Finally, we establish a changing scene verification environment through simulation and experimentation and compare the performance of matching based on Loam and the proposed algorithm. The results show that the algorithm can effectively suppress the matching error caused by the scene change, the root mean square error of positioning is better than 3 cm in the actual scenario, and the positioning accuracy is improved by 67.4% compared with the traditional algorithm.