发表机构
College of Control Science and Engineering, Zhejiang University; Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（浙江大学控制科学与工程学院; 穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于结构化边缘特征的视觉里程计系统，设计专用跟踪与联合优化方法，在室内环境中实现高精度鲁棒的视觉里程计，性能优于或媲美现有先进方法。
AI 中文摘要
本研究提出一种利用图像边缘特征的视觉里程计（VO）系统。边缘是在各类环境中普遍存在的空间表现力线索，能提供丰富的纹理与结构信息。然而，现有基于边缘的VO方法往往未能充分挖掘这一潜力。为此，我们引入一种名为“结构化边缘（organized edges）”的新型特征表示，将不连续的边缘像素转换为序列化簇，从而更有效地保留和利用底层的纹理与结构信息。该表示的另一优势在于，结构化边缘可在多帧间执行边缘级关联，进而建立共可见性图。为实现精确高效的位姿估计，我们基于结构化边缘的特性设计了一系列专用的跟踪与联合优化方法：跟踪阶段，我们构建边缘级而非像素级残差，以实现鲁棒且精确的帧间配准；联合优化阶段，我们引入一种新型保形边缘拟合方法，以及一种基于结构化边缘的光束平差法（Bundle Adjustment, BA），该方法将传统BA问题分解为拟合与配准步骤，以保留结构完整性。基于这些新技术，我们开发了一套仅使用结构化边缘特征的完整VO系统，实现了高效跟踪与精确的局部建图。大量实验表明，该系统在室内环境中具有较高的精度与鲁棒性，性能优于或可与最先进方法相当。源代码可在该https URL获取。
英文摘要
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
