Drift‐free localisation using prior cross‐source map for indoor low‐light environments

Abstract

Abstract In this study, a localisation system without cumulative errors is proposed. First, depth odometry is achieved only by using the depth information from the depth camera. Then the point cloud cross‐source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the map. Furthermore, we fuse the odometry results with the point cloud to map registration results, so the system can operate effectively even if the map is incomplete. The effectiveness of the system for long‐term localisation, localisation in the incomplete map, and localisation in low light through multiple experiments on the self‐recorded dataset is demonstrated. Compared with other methods, the results are better than theirs and achieve high indoor localisation accuracy.

Keywords

• indoor positioning
• robots