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Article: Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments

TitlePixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments
Authors
Issue Date2021
PublisherInstitute 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, Forthcoming How to Cite?
AbstractIn this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. Our approach does not require checkerboards but can achieve pixel-level accuracy by aligning natural edge features in the two sensors. On the theory level, we analyze the constraints imposed by edge features and the sensitivity of calibration accuracy with respect to edge distribution in the scene. On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. Due to the edges’ richness in natural scenes, we have carried out experiments in many indoor and outdoor scenes. The results show that this method has high robustness, accuracy, and consistency. It can promote the research and application of the fusion between LiDAR and camera. We have open sourced our code on GitHub to benefit the community.
Persistent Identifierhttp://hdl.handle.net/10722/301547
ISSN
2021 Impact Factor: 4.321
2020 SCImago Journal Rankings: 1.123

 

DC FieldValueLanguage
dc.contributor.authorYuan, C-
dc.contributor.authorLiu, X-
dc.contributor.authorHong, X-
dc.contributor.authorZhang, F-
dc.date.accessioned2021-08-09T03:40:40Z-
dc.date.available2021-08-09T03:40:40Z-
dc.date.issued2021-
dc.identifier.citationIEEE Robotics and Automation Letters, Forthcoming-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/301547-
dc.description.abstractIn this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. Our approach does not require checkerboards but can achieve pixel-level accuracy by aligning natural edge features in the two sensors. On the theory level, we analyze the constraints imposed by edge features and the sensitivity of calibration accuracy with respect to edge distribution in the scene. On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. Due to the edges’ richness in natural scenes, we have carried out experiments in many indoor and outdoor scenes. The results show that this method has high robustness, accuracy, and consistency. It can promote the research and application of the fusion between LiDAR and camera. We have open sourced our code on GitHub to benefit the community.-
dc.languageeng-
dc.publisherInstitute 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.ispartofIEEE Robotics and Automation Letters-
dc.rightsIEEE 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.titlePixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments-
dc.typeArticle-
dc.identifier.emailZhang, F: fuzhang@hku.hk-
dc.identifier.authorityZhang, F=rp02460-
dc.identifier.hkuros324130-
dc.identifier.volumeForthcoming-
dc.publisher.placeUnited States-

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