Towards Explaining Uncertainty Estimates in Point Cloud Registration

2024/12/29 by Ziyuan Qin, Qin, Ziyuan, Jong‐Seok Lee +3 · 2 citations
Environmental Science · Earth and Planetary Sciences · Computer Science · #Remote Sensing and LiDAR Applications #3D Surveying and Cultural Heritage #Image Processing and 3D Reconstruction

paper · pdf · doi:10.48550/arxiv.2412.20612

Abstract

Iterative Closest Point (ICP) is a commonly used algorithm to estimate transformation between two point clouds. The key idea of this work is to leverage recent advances in explainable AI for probabilistic ICP methods that provide uncertainty estimates. Concretely, we propose a method that can explain why a probabilistic ICP method produced a particular output. Our method is based on kernel SHAP (SHapley Additive exPlanations). With this, we assign an importance value to common sources of uncertainty in ICP such as sensor noise, occlusion, and ambiguous environments. The results of the experiment show that this explanation method can reasonably explain the uncertainty sources, providing a step towards robots that know when and why they failed in a human interpretable manner

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• GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems
• Human-Interpretable Uncertainty Explanations for Point Cloud Registration

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