TY - JOUR
T1 - LiDAR-based place recognition for mobile robots in ground/water surface multiple scenes
AU - Yan, Yaxuan
AU - Zhang, Haiyang
AU - Zhao, Changming
AU - Liu, Xuan
AU - Fu, Siyuan
N1 - Publisher Copyright:
© 2024 Wiley Periodicals LLC.
PY - 2025/3
Y1 - 2025/3
N2 - LiDAR-based 3D place recognition is an essential component of simultaneous localization and mapping systems in multi-scene robotic applications. However, extracting discriminative and generalizable global descriptors of point clouds is still an open issue due to the insufficient use of the information contained in the LiDAR scans in existing approaches. In this paper, we propose a novel spatial-temporal point cloud encoding network for multiple scenes, dubbed STM-Net, to fully fuse the multi-view spatial information and temporal information of LiDAR point clouds. Specifically, we first develop a spatial feature encoding module consisting of the single-view transformer and multi-view transformer. The module learns the correlation both within a single view and between two views by utilizing the multi-layer range images generated by spherical projection and multi-layer bird's eye view images generated by top-down projection. Then in the temporal feature encoding module, we exploit the temporal transformer to mine the temporal information in the sequential point clouds, and a NetVLAD layer is applied to aggregate features and generate sub-descriptors. Furthermore, we use a GeM pooling layer to fuse more information along the time dimension for the final global descriptors. Extensive experiments conducted on unmanned ground/surface vehicles with different LiDAR configurations indicate that our method (1) achieves superior place recognition performance than state-of-the-art algorithms, (2) generalizes well to diverse sceneries, (3) is robust to viewpoint changes, (4) can operate in real-time, demonstrating the effectiveness and satisfactory capability of the proposed approach and highlighting its promising applications in multi-scene place recognition tasks.
AB - LiDAR-based 3D place recognition is an essential component of simultaneous localization and mapping systems in multi-scene robotic applications. However, extracting discriminative and generalizable global descriptors of point clouds is still an open issue due to the insufficient use of the information contained in the LiDAR scans in existing approaches. In this paper, we propose a novel spatial-temporal point cloud encoding network for multiple scenes, dubbed STM-Net, to fully fuse the multi-view spatial information and temporal information of LiDAR point clouds. Specifically, we first develop a spatial feature encoding module consisting of the single-view transformer and multi-view transformer. The module learns the correlation both within a single view and between two views by utilizing the multi-layer range images generated by spherical projection and multi-layer bird's eye view images generated by top-down projection. Then in the temporal feature encoding module, we exploit the temporal transformer to mine the temporal information in the sequential point clouds, and a NetVLAD layer is applied to aggregate features and generate sub-descriptors. Furthermore, we use a GeM pooling layer to fuse more information along the time dimension for the final global descriptors. Extensive experiments conducted on unmanned ground/surface vehicles with different LiDAR configurations indicate that our method (1) achieves superior place recognition performance than state-of-the-art algorithms, (2) generalizes well to diverse sceneries, (3) is robust to viewpoint changes, (4) can operate in real-time, demonstrating the effectiveness and satisfactory capability of the proposed approach and highlighting its promising applications in multi-scene place recognition tasks.
KW - 3D place recognition
KW - multiple scenes
KW - point cloud encoding
KW - spatial-temporal fusion
UR - http://www.scopus.com/inward/record.url?scp=85202891353&partnerID=8YFLogxK
U2 - 10.1002/rob.22423
DO - 10.1002/rob.22423
M3 - Article
AN - SCOPUS:85202891353
SN - 1556-4959
VL - 42
SP - 539
EP - 558
JO - Journal of Field Robotics
JF - Journal of Field Robotics
IS - 2
ER -