Persistent Stereo Visual Localization on Cross-Modal Invariant Map

Ding, X; Wang, Y; Xiong, R; Li, D; Tang, L; Yin, H; Zhao, L

Persistent Stereo Visual Localization on Cross-Modal Invariant Map

Ding, X Wang, Y Xiong, R Li, D Tang, L Yin, H Zhao, L

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Transportation Systems, 2020, 21, (11), pp. 4646-4658
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Ding, X
dc.contributor.author	Wang, Y
dc.contributor.author	Xiong, R
dc.contributor.author	Li, D
dc.contributor.author	Tang, L
dc.contributor.author	Yin, H
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.date.accessioned	2021-03-26T07:11:32Z
dc.date.available	2021-03-26T07:11:32Z
dc.date.issued	2020
dc.identifier.citation	IEEE Transactions on Intelligent Transportation Systems, 2020, 21, (11), pp. 4646-4658
dc.identifier.issn	1524-9050
dc.identifier.issn	1558-0016
dc.identifier.uri	http://hdl.handle.net/10453/147547
dc.description.abstract	Autonomous mobile vehicles are expected to perform persistent and accurate localization with low-cost equipment. To achieve this goal, we propose a stereo camera based visual localization method using a modified laser map, which takes the advantage of both the low cost of camera, and high geometric precision of laser data to achieve long-term performance. Considering that LiDAR and camera give measurements of the same environment in different modalities, the cross-modal invariance is investigated to modify the laser map for visual localization. Specifically, a map learning algorithm is introduced to sample the robust subsets in laser maps that are useful for visual localization using multi-session visual and laser data. Further, a generative map model is derived to describe this cross-modal invariance, based on which two types of measurements are defined to model the laser map points as appropriate visual observations. Tightly coupling these measurements within the local bundle adjustment during online sliding-window based visual odometry, the vehicle can achieve robust localization even one year after the map was built. The effectiveness of the proposed method is evaluated on both the public KITTI datasets and self-collected datasets in our campus, which include seasonal, illumination and object variations. On all experimental localization sessions, our method provides satisfactory results, even when the direction is opposite to that in the mapping session, verifying the superior performance of the laser map based visual localization method.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Intelligent Transportation Systems
dc.relation.isbasedon	10.1109/tits.2019.2942760
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.title	Persistent Stereo Visual Localization on Cross-Modal Invariant Map
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0905 Civil Engineering
utslib.for	1507 Transportation and Freight Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-03-26T07:11:28Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	11

Abstract:

Autonomous mobile vehicles are expected to perform persistent and accurate localization with low-cost equipment. To achieve this goal, we propose a stereo camera based visual localization method using a modified laser map, which takes the advantage of both the low cost of camera, and high geometric precision of laser data to achieve long-term performance. Considering that LiDAR and camera give measurements of the same environment in different modalities, the cross-modal invariance is investigated to modify the laser map for visual localization. Specifically, a map learning algorithm is introduced to sample the robust subsets in laser maps that are useful for visual localization using multi-session visual and laser data. Further, a generative map model is derived to describe this cross-modal invariance, based on which two types of measurements are defined to model the laser map points as appropriate visual observations. Tightly coupling these measurements within the local bundle adjustment during online sliding-window based visual odometry, the vehicle can achieve robust localization even one year after the map was built. The effectiveness of the proposed method is evaluated on both the public KITTI datasets and self-collected datasets in our campus, which include seasonal, illumination and object variations. On all experimental localization sessions, our method provides satisfactory results, even when the direction is opposite to that in the mapping session, verifying the superior performance of the laser map based visual localization method.

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http://hdl.handle.net/10453/147547