LILO: A Novel Lidar–IMU SLAM System With Loop Optimization

Abstract

Light detection and ranging (Lidar) has become the core device in a simultaneous localization and mapping system, which has attracted much attention in recent years. However, the point clouds acquired by Lidar are sparse and mutually uncorrelated, making it difficult to calculate correspondences between consecutive frames and resulting in data drifting. In this situation, we propose a tightly coupled Lidar + IMU fusion system with loop optimization to address the above-mentioned problems. First, the ground Lidar points are segmented and removed. Second, under our framework, an inertial measurement unit is used to deskew the motion distortion of Lidar. Third, a voxel grid filtering process is implemented to further eliminate the redundant points, and feature matching is performed by identifying lines and planes. Finally, loop closure detection is realized to correct pose estimation and localization error. Experimental results on both public dataset and field test demonstrate the effectiveness of our algorithm.