UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning

Ruihao Li, Sen Wang, Zhiqiang Long, Dongbing Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

76 Citations (Scopus)
46 Downloads (Pure)

Abstract

We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVo:one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVoby using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages7286-7291
Number of pages6
ISBN (Electronic)9781538630815
DOIs
Publication statusPublished - 13 Sep 2018

Publication series

NameInternational Conference on Robotics and Automation
PublisherIEEE
ISSN (Electronic)2577-087X

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  • Cite this

    Li, R., Wang, S., Long, Z., & Gu, D. (2018). UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7286-7291). (International Conference on Robotics and Automation). IEEE. https://doi.org/10.1109/ICRA.2018.8461251