# End-to-End Visual Odometry with RNNs and Attention

arXiv: https://arxiv.org/abs/2609.26188 · HTML: https://mlsanity.com/paper/2609.26188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [DOT: Dynamic Object Tracking for Visual SLAM](https://mlsanity.com/paper/2010.00052/) (2020)

## Abstract (arXiv)

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.