Open AccessMonocular Visual Odometry based on joint unsupervised learning of depth and optical flow with geometric constraints
Author(s) -
Xiangrui Meng,
Bo Sun
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1906/1/012056
Subject(s) - visual odometry , epipolar geometry , artificial intelligence , computer vision , optical flow , monocular , computer science , odometry , ground truth , pixel , scale (ratio) , motion (physics) , robot , image (mathematics) , geography , mobile robot , cartography
Inferring camera ego-motion from consecutive images is essential in visual odometry (VO). In this work, we present a jointly unsupervised learning system for monocular VO, consisting of single-view depth, two-view optical flow, and camera-motion estimation module. Our work mitigates the scale drift issue which can further result in a degraded performance in the long-sequence scene. We achieve this by incorporating standard epipolar geometry into the framework. Specifically, we extract correspondences over predicted optical flow and then recover ego-motion. Additionally, we obtain pseudo-ground-truth depth via triangulating 2D-2D pixel matches, which makes the depth scale is closely relevant to the pose. Experimentation on the KITTI driving dataset shows competitive performance compared to established methods.