LiDAR-IMU Tightly-Coupled SLAM Method Based on IEKF and Loop Closure Detection

Abstract

Simultaneous Localization and Mapping (SLAM) technology based on LiDAR can achieve real-time robot positioning and establish environmental maps in unknown environments. LiDAR odometry can achieve accurate pose estimation in short distances or small-scale environments, but the accuracy will decrease with the accumulation of errors. At the same time, under scenes with insufffcient structural features, point cloud-based LiDAR SLAM will show degradation phenomena, leading to failures in localization and mapping. We propose a loop closure detection method based on fusion of Inertial Measurement Unit (IMU) and LiDAR information to reduce system cumulative errors and solve environment degradation problems. Firstly, by fusing LiDAR key feature and IMU information, an odometry calculation method is proposed based on Iterative Extended Kalman Filtering (IEKF) to improve the accuracy of FAST-LIO initial pose. Then in loop closure detection, by considering geometric and intensity information simultaneously, a new scan context global descriptor is constructed from dedistortion feature points of front-end IMU to enhance the accuracy of loop closure detection for original point cloud descriptors. Finally, GTSAM is used for global optimization and GPS constraint is introduced to reduce trajectory drifts, obtaining globally consistent trajectories and mapping. Compared with existing LiDAR SLAM on KITTI dataset and self-collected dataset, proposed method has smaller trajectory errors on KITTI sequences, which reduced by 15% than baseline FAST-LIO2, and average time consumption of loop closure detection is reduced by about 50% than SC-ALOAM, and mapping drifts is reduced, which enhanced mapping accuracy and robustness while ensuring global consistency of constructed maps.