Abstract
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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
