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- Publisher Website: 10.1109/IROS45743.2020.9340790
- Scopus: eid_2-s2.0-85102403973
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Conference Paper: A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs
Title | A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs |
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Authors | |
Keywords | Location awareness Vibrations Laser radar Urban areas Hardware |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393 |
Citation | Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Virtual Conference, Las Vegas, NV, USA, 24 October 2020 - 24 January 2021, p. 4870-4877 How to Cite? |
Abstract | LiDAR is playing a more and more essential role in autonomous driving vehicles for objection detection, self localization and mapping. A single LiDAR frequently suffers from hardware failure (e.g., temporary loss of connection) due to the harsh vehicle environment (e.g., temperature, vibration, etc.), or performance degradation due to the lack of sufficient geometry features, especially for solid-state LiDARs with small field of view (FoV). To improve the system robustness and performance in self-localization and mapping, we develop a decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs. Our proposed framework is based on an extended Kalman filter (EKF), but is specially formulated for decentralized implementation. Such an implementation could potentially distribute the intensive computation among smaller computing devices or resources dedicated for each LiDAR and remove the single point of failure problem. Then this decentralized formulation is implemented on an unmanned ground vehicle (UGV) carrying 5 low-cost LiDARs and moving at 1.3m/s in urban environments. Experiment results show that the proposed method can successfully and simultaneously estimate the vehicle state (i.e., pose and velocity) and all LiDAR extrinsic parameters. The localization accuracy is up to 0.2% on the two datasets we collected. To share our findings and to make contributions to the community, meanwhile enable the readers to verify our work, we will release all our source codes 1 and hardware design blueprint 2 on our Github. |
Persistent Identifier | http://hdl.handle.net/10722/301591 |
ISSN | 2023 SCImago Journal Rankings: 1.094 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lin, J | - |
dc.contributor.author | Liu, X | - |
dc.contributor.author | Zhang, F | - |
dc.date.accessioned | 2021-08-09T03:41:17Z | - |
dc.date.available | 2021-08-09T03:41:17Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Virtual Conference, Las Vegas, NV, USA, 24 October 2020 - 24 January 2021, p. 4870-4877 | - |
dc.identifier.issn | 2153-0858 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301591 | - |
dc.description.abstract | LiDAR is playing a more and more essential role in autonomous driving vehicles for objection detection, self localization and mapping. A single LiDAR frequently suffers from hardware failure (e.g., temporary loss of connection) due to the harsh vehicle environment (e.g., temperature, vibration, etc.), or performance degradation due to the lack of sufficient geometry features, especially for solid-state LiDARs with small field of view (FoV). To improve the system robustness and performance in self-localization and mapping, we develop a decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs. Our proposed framework is based on an extended Kalman filter (EKF), but is specially formulated for decentralized implementation. Such an implementation could potentially distribute the intensive computation among smaller computing devices or resources dedicated for each LiDAR and remove the single point of failure problem. Then this decentralized formulation is implemented on an unmanned ground vehicle (UGV) carrying 5 low-cost LiDARs and moving at 1.3m/s in urban environments. Experiment results show that the proposed method can successfully and simultaneously estimate the vehicle state (i.e., pose and velocity) and all LiDAR extrinsic parameters. The localization accuracy is up to 0.2% on the two datasets we collected. To share our findings and to make contributions to the community, meanwhile enable the readers to verify our work, we will release all our source codes 1 and hardware design blueprint 2 on our Github. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393 | - |
dc.relation.ispartof | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Proceedings | - |
dc.rights | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Proceedings. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©2020 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 | Location awareness | - |
dc.subject | Vibrations | - |
dc.subject | Laser radar | - |
dc.subject | Urban areas | - |
dc.subject | Hardware | - |
dc.title | A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs | - |
dc.type | Conference_Paper | - |
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/IROS45743.2020.9340790 | - |
dc.identifier.scopus | eid_2-s2.0-85102403973 | - |
dc.identifier.hkuros | 324120 | - |
dc.identifier.spage | 4870 | - |
dc.identifier.epage | 4877 | - |
dc.identifier.isi | WOS:000714033802103 | - |
dc.publisher.place | United States | - |