为点云骨架化结果提供几何质量评估标准，助力机器人感知与操作
Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics
- 定义拓扑相似性、边界性、中心性、平滑性四项几何指标
- 构建评分框架，可量化不同场景下骨架化效果
- 开源工具支持算法评估与优化，适合机器人领域研究者
骨架化是理解物体形态的重要工具，在机器人领域有广泛应用。尽管近年来已有多种骨架化算法，但其性能缺乏详细的定量评估。本文系统定义并量化了拓扑相似性、边界性、中心性和平滑性四项几何属性，构建针对点云形状骨架化结果的多维度评分框架，适用于物体操作与移动机器人导航等不同场景。同时，我们发布了一个开源工具，供研究社区评估和改进骨架模型。最后，通过多种机器人应用场景验证了所提评估方法的有效性与敏感性。
原文摘要 · Abstract (English)
Skeletonization is a powerful tool for shape analysis, rooted in the inherent instinct to understand an object's morphology. It has found applications across various domains, including robotics. Although skeletonization algorithms have been studied in recent years, their performance is rarely quantified with detailed numerical evaluations. This work focuses on defining and quantifying geometric properties to systematically score the skeletonization results of point cloud shapes across multiple aspects, including topological similarity, boundedness, centeredness, and smoothness. We introduce these representative metric definitions along with a numerical scoring framework to analyze skeletonization outcomes concerning point cloud data for different scenarios, from object manipulation to mobile robot navigation. Additionally, we provide an open-source tool to enable the research community to evaluate and refine their skeleton models. Finally, we assess the performance and sensitivity of the proposed geometric evaluation methods from various robotic applications.
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