Unsupervised Geometry-Aware Deep LiDAR Odometry

Younggun Cho, Giseop Kim, Ayoung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

55 Scopus citations

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 languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2145-2152
Number of pages8
ISBN (Electronic)9781728173955
DOIs
StatePublished - May 2020
Externally publishedYes
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: 31 May 202031 Aug 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Country/TerritoryFrance
CityParis
Period31/05/2031/08/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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