ROEVO: Robust Organized Edge Feature-Based Visual Odometry Using RGB-D Cameras
Liu, Mingrui Zuo, Xingxing Huang, Renlang Zhao, Minglei Chen, Jiming Li, Liang
Liu, Mingrui
Zuo, Xingxing
Huang, Renlang
Zhao, Minglei
Chen, Jiming
Li, Liang
Supervisor
Department
Robotics
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Type
Journal article
Date
2025
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Language
English
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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 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.
Citation
M. Liu, X. Zuo, R. Huang, M. Zhao, J. Chen and L. Li, "ROEVO: Robust Organized Edge Feature-Based Visual Odometry Using RGB-D Cameras," in IEEE Transactions on Robotics, doi: 10.1109/TRO.2025.3595702
Source
IEEE Transactions on Robotics
Conference
Keywords
Bundle adjustment, coarse-to-fine tracking, organized edge features, RGB-D visual odometry (VO)
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Publisher
IEEE