ReLoc-Aligner : Orientation-aware Scene Descriptor for Re-Localization within a 3D Point Cloud Map

Abstract

We propose a new orientation-aware scene descriptor ReLoc-Aligner for re-localization of a 3D point cloud. Re-localization within a 3D point cloud map is crucial for conducting Simultaneous Localization and Mapping (SLAM). Existing re-localization or place recognition methods of 3D LiDAR sensor data aim to estimate the current position of the sensor robustly to orientation changes. However, they do not determine the current orientation of the sensor within a 3D point cloud map, which limits their applications to relocalization or loop closing in SLAM. On the other hand, existing methods capable of orientation estimation tend to be slower than them. Our scene descriptor has a property of orientation awareness that enables us to extract the orientation difference between two scans directly from the descriptor. This is useful for the registration of point clouds from a good initial estimate, which leads to better re-localization of a scan. We propose a training method for the new descriptor. In addition, we develop fast querying and re-localization methods using the descriptors. Intensive experiments demonstrate that the proposed method is superior to the existing state-of-the-art methods in both place recognition and orientation estimation.