End-to-End Visual Odometry with RNNs and Attention
cs.CV
Submitted: 2026-08-10
Updated: 2026-08-10
License: http://creativecommons.org/licenses/by/4.0/
The gist: Video Odometry (VO) is the process of estimating the ego-motion of an object by analyzing visual information such as a sequence of frames from one or multiple cameras.
Terminology
Abstract
Video Odometry (VO) is the process of estimating the ego-motion of an object by analyzing visual information such as a sequence of frames from one or multiple cameras. It has been a popular research topic in computer vision and robotics, and its applications include mobile robotic systems as well as autonomous driving. In this project, we investigate existing end-to-end deep-learning approaches to VO, and propose a novel temporal attention-based model to improve upon the baseline. In addition, while the vast majority of existing deep-learning-based approaches to VO are trained on driving data, we investigate the performance of deep-learning-based VO to the more dynamic and complex problem of hand-held cameras.
Sources
- DOT: Dynamic Object Tracking for Visual SLAM
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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