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- Publisher Website: 10.1109/LRA.2021.3062815
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Article: BALM: Bundle Adjustment for Lidar Mapping
Title | BALM: Bundle Adjustment for Lidar Mapping |
---|---|
Authors | |
Keywords | Bundle adujustment lidar localization mapping SLAM |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE |
Citation | IEEE Robotics and Automation Letters, 2021, v. 6 n. 2, p. 3184-3191 How to Cite? |
Abstract | A local Bundle Adjustment (BA) on a sliding window of keyframes has been widely used in visual SLAM and proved to be very effective in lowering the drift. But in lidar SLAM, BA method is hardly used because the sparse feature points (e.g., edge and plane) make the exact point matching impossible. In this letter, we formulate the lidar BA as minimizing the distance from a feature point to its matched edge or plane. Unlike the visual SLAM (and prior plane adjustment method in lidar SLAM) where the feature has to be co-determined along with the pose, we show that the feature can be analytically solved and removed from the BA, the resultant BA is only dependent on the scan poses. This greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used. To speedup the optimization, we derive the analytical derivatives of the cost function, up to second order, in closed form. Moreover, we propose a novel adaptive voxelization method to search feature correspondence efficiently. The proposed formulations are incorporated into a LOAM back-end for map refinement. Results show that, although as a back-end, the local BA can be solved very efficiently, even in real-time at 10 Hz when optimizing 20 scans of point-cloud. The local BA also considerably lowers the LOAM drift. Our implementation of the BA optimization and LOAM are open-sourced to benefit the community. |
Persistent Identifier | http://hdl.handle.net/10722/301528 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.119 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Z | - |
dc.contributor.author | Zhang, F | - |
dc.date.accessioned | 2021-08-09T03:40:23Z | - |
dc.date.available | 2021-08-09T03:40:23Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Robotics and Automation Letters, 2021, v. 6 n. 2, p. 3184-3191 | - |
dc.identifier.issn | 2377-3766 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301528 | - |
dc.description.abstract | A local Bundle Adjustment (BA) on a sliding window of keyframes has been widely used in visual SLAM and proved to be very effective in lowering the drift. But in lidar SLAM, BA method is hardly used because the sparse feature points (e.g., edge and plane) make the exact point matching impossible. In this letter, we formulate the lidar BA as minimizing the distance from a feature point to its matched edge or plane. Unlike the visual SLAM (and prior plane adjustment method in lidar SLAM) where the feature has to be co-determined along with the pose, we show that the feature can be analytically solved and removed from the BA, the resultant BA is only dependent on the scan poses. This greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used. To speedup the optimization, we derive the analytical derivatives of the cost function, up to second order, in closed form. Moreover, we propose a novel adaptive voxelization method to search feature correspondence efficiently. The proposed formulations are incorporated into a LOAM back-end for map refinement. Results show that, although as a back-end, the local BA can be solved very efficiently, even in real-time at 10 Hz when optimizing 20 scans of point-cloud. The local BA also considerably lowers the LOAM drift. Our implementation of the BA optimization and LOAM are open-sourced to benefit the community. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE | - |
dc.relation.ispartof | IEEE Robotics and Automation Letters | - |
dc.rights | IEEE Robotics and Automation Letters. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Bundle adujustment | - |
dc.subject | lidar | - |
dc.subject | localization | - |
dc.subject | mapping | - |
dc.subject | SLAM | - |
dc.title | BALM: Bundle Adjustment for Lidar Mapping | - |
dc.type | Article | - |
dc.identifier.email | Zhang, F: fuzhang@hku.hk | - |
dc.identifier.authority | Zhang, F=rp02460 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/LRA.2021.3062815 | - |
dc.identifier.scopus | eid_2-s2.0-85102271353 | - |
dc.identifier.hkuros | 324109 | - |
dc.identifier.volume | 6 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 3184 | - |
dc.identifier.epage | 3191 | - |
dc.identifier.isi | WOS:000633394300004 | - |
dc.publisher.place | United States | - |