Robust High Accuracy VisualInertialLaser SLAM System In recent years, many excellent works on visualinertial SLAM and laserbased SLAM have been proposed. Although inertial measurement unit (IMU) significantly improve the motion estimate performance by reducing the impact of illumination variation or textureless region on visual tracking, tracking failures occur when in such an environment for a long time. Similarly, when in structureless environments, laser module will fail since lack of sufficient geometric features. Besides, motion estimation by moving lidar has the problem of distortion since range measurements are received continuously. To solve these problems, we propose a robust and highaccuracy visualinertiallaser SLAM system. The system starts with a visualinertial tightlycoupled method for motion estimation, followed by scan matching to further optimize the estimation and register point cloud on the map. Furthermore, we enable modules to be adjusted automatically and flexibly. That is, when one of these modules fails, the remaining modules will undertake the motiontracking task. For further improving the accuracy, loop closure and proximity detection are implemented to eliminate drift accumulation. When loop or proximity is detected, we perform six degreeoffreedom (6DOF) pose graph optimization to achieve the global consistency. The performance of our system is verified on public dataset, and the experimental results show that the proposed method achieves superior accuracy against other stateoftheart algorithms.

Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases. VILSLAM accomplishes this by incorporating tightlycoupled stereo visual inertial odometry (VIO) with LiDAR mapping and LiDAR enhanced visual loop closure. The system generates loopclosure corrected 6DOF LiDAR poses in realtime and lcm voxel dense maps near realtime. VILSLAM demonstrates improved accuracy and robustness compared to stateoftheart LiDAR methods.