Tightly coupled LiDAR inertial odometry based on point-line-surface feature matching
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.
simultaneous localization and mapping(SLAM)LiDARtightly-coupledloop detection
万方数据
维普
