Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics

2025-03-29

AI Summary

To address the lack of objective, reproducible geometric evaluation for point-cloud skeletonization in robotics applications, this work introduces the first systematic, multi-dimensional evaluation framework. It comprises four geometric quality metrics: topological similarity, boundedness, centrality, and smoothness, unified within a single numerical scoring scheme. Methodologically, the framework integrates point-cloud topological analysis, signed distance field modeling, quantitative centrality deviation estimation, and curvature-driven smoothness measurement. We implement an open-source Python evaluation toolkit to support reproducible assessment. Extensive validation on real-world point-cloud data across robotic tasks—including grasping and navigation—demonstrates that our framework significantly improves skeleton quality discrimination accuracy and interpretability. It enables cross-task performance sensitivity analysis and has been adopted by the research community for algorithm development and benchmarking.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: State Estimation
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Quantify geometric properties of skeletonization in point clouds
• Evaluate skeletonization quality via topological and shape metrics
• Provide open-source tool for skeleton model assessment

Innovation

• Defining geometric metrics for skeletonization evaluation
• Introducing numerical scoring framework for point clouds
• Providing open-source tool for community assessment