File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/LRA.2023.3238902
- Scopus: eid_2-s2.0-85147261679
- WOS: WOS:000936534100003
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Large-Scale LiDAR Consistent Mapping using Hierarchical LiDAR Bundle Adjustment
Title | Large-Scale LiDAR Consistent Mapping using Hierarchical LiDAR Bundle Adjustment |
---|---|
Authors | |
Keywords | localization Mapping SLAM |
Issue Date | 23-Jan-2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Robotics and Automation Letters, 2023, v. 8, n. 3, p. 1523-1530 How to Cite? |
Abstract | Reconstructing an accurate and consistent large-scale LiDAR point cloud map is crucial for robotics applications. The existing solution, pose graph optimization, though it is time-efficient, does not directly optimize the mapping consistency. LiDAR bundle adjustment (BA) has been recently proposed to resolve this issue; however, it is too time-consuming on large-scale maps. To mitigate this problem, this paper presents a globally consistent and efficient mapping method suitable for large-scale maps. Our proposed work consists of a bottom-up hierarchical BA and a top-down pose graph optimization, which combines the advantages of both methods. With the hierarchical design, we solve multiple BA problems with a much smaller Hessian matrix size than the original BA; with the pose graph optimization, we smoothly and efficiently update the LiDAR poses. The effectiveness and robustness of our proposed approach have been validated on multiple spatially and timely large-scale public spinning LiDAR datasets, i.e., KITTI, MulRan and Newer College, and self-collected solid-state LiDAR datasets under structured and unstructured scenes. With proper setups, we demonstrate our work could generate a globally consistent map with around 12 % of the sequence time. |
Persistent Identifier | http://hdl.handle.net/10722/331150 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.119 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, XY | - |
dc.contributor.author | Liu, Z | - |
dc.contributor.author | Kong, FZ | - |
dc.contributor.author | Zhang, F | - |
dc.date.accessioned | 2023-09-21T06:53:10Z | - |
dc.date.available | 2023-09-21T06:53:10Z | - |
dc.date.issued | 2023-01-23 | - |
dc.identifier.citation | IEEE Robotics and Automation Letters, 2023, v. 8, n. 3, p. 1523-1530 | - |
dc.identifier.issn | 2377-3766 | - |
dc.identifier.uri | http://hdl.handle.net/10722/331150 | - |
dc.description.abstract | <p>Reconstructing an accurate and consistent large-scale LiDAR point cloud map is crucial for robotics applications. The existing solution, pose graph optimization, though it is time-efficient, does not directly optimize the mapping consistency. LiDAR bundle adjustment (BA) has been recently proposed to resolve this issue; however, it is too time-consuming on large-scale maps. To mitigate this problem, this paper presents a globally consistent and efficient mapping method suitable for large-scale maps. Our proposed work consists of a bottom-up hierarchical BA and a top-down pose graph optimization, which combines the advantages of both methods. With the hierarchical design, we solve multiple BA problems with a much smaller Hessian matrix size than the original BA; with the pose graph optimization, we smoothly and efficiently update the LiDAR poses. The effectiveness and robustness of our proposed approach have been validated on multiple spatially and timely large-scale public spinning LiDAR datasets, i.e., KITTI, MulRan and Newer College, and self-collected solid-state LiDAR datasets under structured and unstructured scenes. With proper setups, we demonstrate our work could generate a globally consistent map with around 12 % of the sequence time.<br></p> | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Robotics and Automation Letters | - |
dc.subject | localization | - |
dc.subject | Mapping | - |
dc.subject | SLAM | - |
dc.title | Large-Scale LiDAR Consistent Mapping using Hierarchical LiDAR Bundle Adjustment | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/LRA.2023.3238902 | - |
dc.identifier.scopus | eid_2-s2.0-85147261679 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 1523 | - |
dc.identifier.epage | 1530 | - |
dc.identifier.eissn | 2377-3766 | - |
dc.identifier.isi | WOS:000936534100003 | - |
dc.identifier.issnl | 2377-3766 | - |