LiDAR odometry is disturbed by environmental noise outdoors,which cause low scan matching precision,accumulated error caused by scan matching leads to poor positioning precision of simultaneous localization and mapping(SLAM)in large-scale scenes. Aiming at the above problem,a tightly-coupled LiDAR inertial odometry (TP-LIO )based on point-line-surface feature matching is proposed. On the basis of inertial measurement unit(IMU)pre-integration point cloud de-distortion,point-line and point-surface distances are used to construct pose estimation cost functions to obtain carrier motion estimation. Combined with IMU pre-integration and Scan-Context loop detection,global optimization is performed through factor graphs to achieve tight coupling of IMU and LiDAR data. In the KITTI dataset and real vehicle experiments,TP-LIO,ALOAM and LeGO-LOAM are compared and verified. The results show that TP-LIO has smaller cumulative error and higher positioning precision in a wide range of scenes.
关键词
同步定位与建图/激光雷达/紧耦合/回环检测
Key words
simultaneous localization and mapping(SLAM)/LiDAR/tightly-coupled/loop detection
维普
万方数据