HOME：用于结构化和无纹理视频的鲁棒霍夫空间匹配方法
HOME: Robust Hough-space Matching Method for Structured and Textureless Videos
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中文总结 AI 辅助
针对机器人定位中基于点的特征在结构化或无纹理环境中失效的问题，提出HOME框架，将图像转换到霍夫空间，通过一维点匹配实现高效线匹配，经单应性估计验证，其匹配精度高、速度快，扩展到3D姿态估计前景广阔。
中文摘要 AI 辅助
机器人定位的视觉前端通常依赖基于点的特征，如ORB，在由强线性结构或无纹理表面主导的结构化环境中经常失败。基于线的SLAM系统虽能缓解此问题，但传统线提取和描述算法计算量过大。为此，我们提出HOME，一个超轻量级、无需训练的特征匹配框架。HOME将图像转换到霍夫空间，把全局线性结构映射到稳定的局部极值作为关键点，将复杂的线匹配转化为高效的一维点匹配。所提出的一维径向描述符在数学上保证旋转和平移不变性，无需显式方向估计开销。本文以单应性估计为例验证HOME的匹配精度和效率。大量评估表明，HOME在基于点的方法失败的具有挑战性的场景中实现了鲁棒配准，速度比现有的基于线的方法快得多。将这个鲁棒匹配引擎扩展到完整的3D姿态估计仍然是一个非常有前途的未来方向。
英文摘要
Visual front-ends for robotic localization typically rely on point-based features such as Oriented FAST and Rotated BRIEF (ORB), which frequently fail in structured environments dominated by strong linear structures or textureless surfaces. While line-based Simultaneous Localization and Mapping (SLAM) systems mitigate this by utilizing line segments, conventional line extraction and description algorithms are computationally prohibitive for real-time edge robotics. To address this fundamental bottleneck, we propose HOME (Hough-space One-dimensional Matching of Extrema), an ultra-lightweight, training-free feature matching framework. HOME transforms images into Hough space, mapping global linear structures to stable local extrema, which serve as keypoints, thereby reformulating complex line matching into highly efficient one-dimensional point matching. The proposed 1D radial descriptor mathematically guarantees rotational and translational invariance without the overhead of explicit orientation estimation. As a proof of concept to validate the matching accuracy and efficiency of HOME, this paper focuses on homography estimation. Extensive evaluations demonstrate that HOME achieves robust registration in challenging scenarios where point-based methods fail, operating at a much faster speed than existing line-based methods. Extending this robust matching engine to full 3D pose estimation remains a highly promising future direction.
