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.

LMVISLAM: Robust LowLight Monocular VisualInertial Simultaneous Localization and Mapping Visualinertial simultaneous localization and mapping (SLAM) shows significant progress in recent years due to the complementary nature of the visual and inertial sensor but still, challenges remain for lowlight environments. Recent visualinertial SLAMs often drift or even fail in lowlight conditions due to insufficient 3D2D correspondences for bundle adjustment. To address the issue, this paper performs image preprocessing firstly with a united image enhancement method involving adaptive gamma correction and contrast limited adaptive histogram equalization, which could ameliorate the brightness and contrast of the image greatly. Moreover, we track features using optical flow for adequate point correspondences in dimlight environments, and supplement the corresponding map points continually by insert keyframe and triangulation to keep tracking. Finally, we construct a tightlycoupled nonlinear optimization model, which combines a feature reprojection error on point correspondences and IMU measurement by preintegration to constrain and compensate each other for more accurate pose estimation. We validate the performance of our algorithm on public dataset and realword experiments with a mobile robot, including dark laboratory, etc., and compare against existing stateoftheart visualinertial algorithms. Experimental results indicate our algorithm outperforms other stateoftheart SLAMs in accuracy and robustness, and works reliably well for both general and lowlight environments.