Fusion of binocular vision, 2D LiDAR and IMU for outdoor localization and indoor planar mapping

Abstract Emergent fields such as Internet of Things applications, driverless cars, and indoor mobile robots have an increasing demand for simultaneous localization and mapping (SLAM) technology. In this study, we design a SLAM scheme called BVLI-SLAM based on binocular vision, 2D LIDAR, and an IMU sensor. The pose estimation provided by vision and the IMU can provide better initial values for the 2D LIDAR mapping algorithm and improve the mapping effect. LIDAR can also assist vision to provide better plane and yaw angle constraints in weak texture areas and obtain higher precision 6-DOF pose. BVLI-SLAM uses graph optimization to fuse the three data of the IMU, binocular camera, and laser. The IMU pre-integration combines the visual reprojection error and the laser matching error to form an error equation, which is processed by a sliding window-based bundle adjustment optimization to calculate the pose in real-time. Outdoor experiments based on kitti datasets and indoor experiments based on trolley mobile measurement platform show that BVLI-SLAM has different degrees of improvement in mapping effect, positioning accuracy, and robustness compared with VINS-Fusion and Cartographer and can solve the problem of positioning and plane mapping in indoor complex scenes.