ROEVO: Robust Organized Edge Feature-based Visual Odometry Using RGB-D Cameras
2026-08-10 • Robotics
Robotics
AI summaryⓘ
The authors developed a visual odometry system that uses image edges—lines and boundaries in pictures—to better understand and track movement. They grouped individual edge pixels into connected clusters called organized edges, which helps keep important shape and texture information intact. This method allows the system to track these edges across multiple frames and build a graph of visible features. They also designed special ways to estimate the camera's position by fitting and registering these edges efficiently. Their approach works well indoors and matches or beats other leading methods.
visual odometryedge featuresorganized edgespose estimationedge trackingbundle adjustmentco-visibility graphimage processingfeature association
Authors
Mingrui Liu, Xingxing Zuo, Renlang Huang, Minglei Zhao, Jiming Chen, Liang Li
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