EIL-SLAM: Depth-enhanced edge-based infrared-LiDAR SLAM

Traditional simultaneous localization and mapping (SLAM) approaches that utilize visible cameras or light detection and rangings (LiDARs) frequently fail in dusty, low-textured, or completely dark environments. To address this problem, this study proposes a novel approach by tightly coupling perception data from a thermal infrared camera and a LiDAR based on the advantages of the former. However, applying a thermal infrared camera directly to existing SLAM frameworks is difficult because of the sensor differences. Thus, a new infrared-visual odometry method is developed by utilizing edge points as features to ensure the robustness of the state estimation. Furthermore, an edge-based infrared-LiDAR SLAM framework is developed to generate a dense depth map for recovering visual scale and to provide real-time pose estimation at the same time throughout the day. An infrared-visual and LiDAR-integrated place recognition method is also introduced to achieve robust loop closure. Finally, several experiments are performed to illustrate the effectiveness of the proposed approach.