Tightly-coupled stereo visual-inertial-LiDAR SLAM based on graph optimization

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.