将鲁棒定位算法CLAP扩展至3D与图像拼接，连接RANSAC与霍夫变换。
A Generalization of CLAP from 3D Localization to Image Processing, A Connection With RANSAC & Hough Transforms
- 基于聚类的去噪策略替代传统误差验证，提升抗干扰能力。
- 在3D定位与图像拼接任务中验证了算法有效性，稳定处理噪声数据。
- 适用于多领域中的不确定性问题，适合机器人视觉与图像处理研究者。
此前我们提出了名为CLAP（Clustering to Localize Across $n$ Possibilities）的2D定位算法，并在2024年国际自主人形足球竞赛RoboCup中助力夺冠。该算法以对异常值具有强鲁棒性著称，通过聚类抑制噪声并缓解错误特征匹配的影响。此聚类策略为传统基于重投影误差的异常值剔除方法（如RANSAC）提供了一种替代方案。本文将CLAP推广至更通用的框架，涵盖3D定位与图像拼接任务，并揭示其与RANSAC及霍夫变换之间的内在联系。该通用化方法可广泛应用于多个领域，是应对噪声与不确定性的有效工具。
原文摘要 · Abstract (English)
In previous work, we introduced a 2D localization algorithm called CLAP, Clustering to Localize Across $n$ Possibilities, which was used during our championship win in RoboCup 2024, an international autonomous humanoid soccer competition. CLAP is particularly recognized for its robustness against outliers, where clustering is employed to suppress noise and mitigate against erroneous feature matches. This clustering-based strategy provides an alternative to traditional outlier rejection schemes such as RANSAC, in which candidates are validated by reprojection error across all data points. In this paper, CLAP is extended to a more general framework beyond 2D localization, specifically to 3D localization and image stitching. We also show how CLAP, RANSAC, and Hough transforms are related. The generalization of CLAP is widely applicable to many different fields and can be a useful tool to deal with noise and uncertainty.
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