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arXiv · 2609.26188

End-to-End Visual Odometry with RNNs and Attention

Ruiyu Li·Yinjia Liu·Alexander Yu
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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.

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Ruiyu Li, Yinjia Liu, Alexander Yu. 2026-08-10. End-to-End Visual Odometry with RNNs and Attention. https://arxiv.org/abs/2609.26188

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