一种基于地磁信号的激光同时定位与建图的闭环检测方法

A Lidar Simultaneous Localization and Mapping Closed-loop Detection Method Based on Geomagnetic Signal

激光同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术在激光干扰或结构高度相似的环境中,容易产生闭环误检。针对这一问题,该研究提出一种闭环粗匹配与地磁特征筛选闭环检测算法。通过在闭环检测环节中加入地磁匹配算法,对候选闭环检测位姿节点集进一步筛选,降低了传统激光闭环检测的误检现象,并对定位与建图环境中由于反射与透射干扰而引起的误检测与建图失真进行修正。该研究采集了真实的激光点云与地磁信号数据集,并将所研究算法与传统激光SLAM进行了对比。实验结果显示,该算法在匹配速度和准确率上都有明显提升,与Google的Cartographer算法相比,在闭环检测速度上提升了31%,在0.8召回率的情况下闭环检测的误检率降低了23%,提升了SLAM技术在激光干扰条件下工作的稳定性。

Simultaneous localization and mapping(SLAM) technique is sensitive to the environments with laser interference or structural similarity, which usually cause the closed-loop misdetection. To solve this problem, this study proposed a closed-loop coarse matching and geomagnetic feature screening closed-loop detection algorithm. By adding a geomagnetic matching algorithm to the closed-loop detection link to further filter the candidate closed-loop detection pose node set, the false detection phenomenon of traditional lidar closed-loop detection can be reduced. It can also correct the false detection and reflection caused by reflection and transmission interference in the positioning and mapping environment, as well as the map image distortion. This study verified the performance of the algorithm through the lidar point cloud and geomagnetic signal data sets collected in the real environment. Compared with traditional lidar SLAM methods, the proposed method outperformed in both matching speed and accuracy. Compared with Google Cartographer algorithm, the algorithm can improve the closed-loop detection speed by 31%, and the false detection rate of closed-loop detection can be reduced by 23% under the condition of 0.8 recall rate. This research expands the application scenarios of SLAM technology, so that the lidar SLAM has better positioning and mapping effects in the scene contains laser interference.