Abstract: Simultaneous localization and mapping (SLAM) technology based on a single sensor has gradually been unable to meet the increasingly complex application scenarios of the intelligent mobile carriers such as mobile robots,unmanned aerial vehicles,and self-driving cars.In order to further improve the localization and mapping performance of the mobile carriers in complex environments,multi-sensor fusion SLAM has become a hotspot of current research.In this contribution,we present a graph-optimization based and tightly-coupled stereo visual-inertial-LiDAR SLAM termed S-VIL SLAM,which integrates the LiDAR observations into a visual-inertial system.In this work,the IMU measurements,visual features,and laser point cloud features are jointly optimized in a sliding window.Moreover,a vision enhanced loop-closure algorithm of LiDAR is designed in this paper by using the complementary characteristics between vision and LiDAR,which further improves the global positioning and mapping accuracy of the multi-sensor fusion SLAM.We perform vehicle-borne experiments in outdoor environments to assess the performance of the proposed approach.The experimental results indicate that the proposed S-VIL odometry outperforms the state-of-the-art tightly coupled visual-inertial odometry (VIO) and LiDAR odometry in terms of pose estimation accuracy.The proposed loop-closure algorithm can effectively detect the loop closure of trajectories in large-scale scenes and achieve high-precision pose graph optimization.The point cloud map after loop closure optimization has good resolution and global consistency.

Key words: SLAM, graph optimization, VIO, LiDAR, multi-sensor fusion