# EdgeVO: An Efficient and Accurate Edge-based Visual Odometry

arXiv: https://arxiv.org/abs/2302.09493 · HTML: https://mlsanity.com/paper/2302.09493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [EPO: Boosting 3D Foundation Models with Edge-based Pose Optimization](https://mlsanity.com/paper/2607.00579/) (2026)

## Abstract (arXiv)

Visual odometry is important for plenty of applications such as autonomous vehicles, and robot navigation. It is challenging to conduct visual odometry in textureless scenes or environments with sudden illumination changes where popular feature-based methods or direct methods cannot work well. To address this challenge, some edge-based methods have been proposed, but they usually struggle between the efficiency and accuracy. In this work, we propose a novel visual odometry approach called textitEdgeVO, which is accurate, efficient, and robust. By efficiently selecting a small set of edges with certain strategies, we significantly improve the computational efficiency without sacrificing the accuracy. Compared to existing edge-based method, our method can significantly reduce the computational complexity while maintaining similar accuracy or even achieving better accuracy. This is attributed to that our method removes useless or noisy edges. Experimental results on the TUM datasets indicate that EdgeVO significantly outperforms other methods in terms of efficiency, accuracy and robustness.