Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles

Chengze Wang, Yuan Yuan, Qi Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles. When modelling the geometric consistency within adjacent frames, most deep VO methods ignore the temporal continuity of the camera pose, which results in a very severe jagged fluctuation in the velocity curves. With the observation that road vehicles tend to perform smooth dynamic characteristics in most of the time, we design the inertia loss function to describe the abnormal motion variation, which assists the model to learn the consecutiveness from long-term camera ego-motion. Based on the recurrent convolutional neural network (RCNN) architecture, our method implicitly models the dynamics of road vehicles and the temporal consecutiveness by the extended Long Short-Term Memory (LSTM) block. Furthermore, we develop the dynamic hard-edge mask to handle the non-consistency in fast camera motion by blocking the boundary part and which generates more efficiency in the whole non-consistency mask. The proposed method is evaluated on the KITTI dataset, and the results demonstrate state-of-the-art performance with respect to other monocular deep VO and SLAM approaches.

源语言英语
主期刊名2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
2252-2256
页数5
ISBN(电子版)9781479981311
DOI
出版状态已出版 - 5月 2019
活动44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, 英国
期限: 12 5月 201917 5月 2019

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2019-May
ISSN(印刷版)1520-6149

会议

会议44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
国家/地区英国
Brighton
时期12/05/1917/05/19

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