ROEVO: Robust Organized Edge Feature-based Visual Odometry Using RGB-D Cameras

2026-08-10

AI Summary

This work addresses the limitations of existing edge-based visual odometry methods, which fail to effectively exploit the structural and textural information embedded in edges, thereby compromising pose estimation accuracy and robustness. To overcome this, we propose a novel visual odometry system based on organized edge features: discrete edges are aggregated into ordered clusters to construct an edge-level co-visibility graph, and we introduce edge-level residual tracking, conformal edge fitting, and a factorized Bundle Adjustment optimization framework. Leveraging RGB-D input, our approach employs an organized edge representation combined with joint optimization to significantly enhance both pose estimation and local mapping performance. Experimental results demonstrate that our method achieves state-of-the-art or superior accuracy in indoor environments, and we publicly release the complete implementation.

Abstract

This work presents a visual odometry (VO) system that leverages image edge features. Edges are spatially expressive cues commonly present across diverse environments, offering rich textural and structural information. However, existing edge-based VO methods often fail to fully exploit this potential. To this end, we introduce a novel feature representation termed \textit{organized edges}, which transforms disjoint edge pixels into sequentialized clusters, enabling more effective retention and utilization of the underlying textural and structural information. Another nice property of this formulation is that organized edges can perform edge-level association across multiple frames, enabling the establishment of a co-visibility graph. To achieve precise and efficient pose estimation, we propose a range of particularly designed tracking and joint optimization methods based on the characteristics of organized edges. For tracking, we formulate edge-wise rather than pixel-wise residuals to achieve robust and accurate inter-frame registration. For joint optimization, we introduce a novel shape-preserving edge-fitting method and an organized edge-based Bundle Adjustment (BA) approach, which decomposes the traditional BA problem into fitting and registration to preserve the structural integrity. Based on these novel techniques, we develop a complete VO system that exclusively employs organized edge features, achieving efficient tracking and precise local mapping. Extensive experiments demonstrate its accuracy and robustness in indoor environments, outperforming or achieving comparable performance to state-of-the-art methods. The source code is publicly available at https://github.com/liumingrui814/ROEVO

Problem

• visual odometry
• edge features
• RGB-D cameras
• structured information
• robustness

Innovation

• organized edges
• edge-based visual odometry
• shape-preserving edge fitting
• edge-level association
• bundle adjustment