EdgeVO: An Efficient and Accurate Edge-based Visual Odometry

TL;DR

EdgeVO is a novel edge-based visual odometry method that improves efficiency and accuracy by selectively removing noisy edges, demonstrating superior performance on TUM datasets for autonomous navigation.

Contribution

The paper introduces EdgeVO, a new edge-based visual odometry approach that balances efficiency and accuracy through strategic edge selection, outperforming existing methods.

Findings

Significantly reduces computational complexity.

Maintains or improves accuracy compared to existing methods.

Demonstrates robustness and efficiency on TUM datasets.

Abstract

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 \textit{EdgeVO}, 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.