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.
Original language | English |
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Title of host publication | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2145-2152 |
Number of pages | 8 |
ISBN (Electronic) | 9781728173955 |
DOIs | |
State | Published - May 2020 |
Externally published | Yes |
Event | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France Duration: 31 May 2020 → 31 Aug 2020 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
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ISSN (Print) | 1050-4729 |
Conference
Conference | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 |
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Country/Territory | France |
City | Paris |
Period | 31/05/20 → 31/08/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.