DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Younggun | ko |
dc.contributor.author | Kim, Giseop | ko |
dc.contributor.author | Kim, Ayoung | ko |
dc.date.accessioned | 2020-12-21T09:30:13Z | - |
dc.date.available | 2020-12-21T09:30:13Z | - |
dc.date.created | 2020-11-30 | - |
dc.date.created | 2020-11-30 | - |
dc.date.issued | 2020-05-31 | - |
dc.identifier.citation | IEEE International Conference on Robotics and Automation (ICRA), pp.2145 - 2152 | - |
dc.identifier.issn | 1050-4729 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278856 | - |
dc.description.abstract | Learning-based ego-motion estimation approaches have recently drawn strong interest from researchers, mostly focusing on visual perception. A few learning-based approaches using Light Detection and Ranging (LiDAR) have been re-ported; however, they heavily rely on a supervised learning manner. Despite the meaningful performance of these approaches, supervised training requires ground-truth pose labels, which is the bottleneck for real-world applications. Differing from these approaches, we focus on unsupervised learning for LiDAR odometry (LO) without trainable labels. Achieving trainable LO in an unsupervised manner, we introduce the uncertainty-aware loss with geometric confidence, thereby al-lowing the reliability of the proposed pipeline. Evaluation on the KITTI, Complex Urban, and Oxford RobotCar datasets demonstrate the prominent performance of the proposed method compared to conventional model-based methods. The proposed method shows a comparable result against SuMa (in KITTI), LeGO-LOAM (in Complex Urban), and Stereo-VO (in Oxford RobotCar). The video and extra-information of the paper are described in https://sites.google.com/view/deeplo. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Unsupervised Geometry-Aware Deep LiDAR Odometry | - |
dc.type | Conference | - |
dc.identifier.wosid | 000712319501081 | - |
dc.identifier.scopusid | 2-s2.0-85092695198 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2145 | - |
dc.citation.endingpage | 2152 | - |
dc.citation.publicationname | IEEE International Conference on Robotics and Automation (ICRA) | - |
dc.identifier.conferencecountry | FR | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/ICRA40945.2020.9197366 | - |
dc.contributor.localauthor | Kim, Ayoung | - |
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