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arXiv · 2509.18786

Human-Interpretable Uncertainty Explanations for Point Cloud Registration

Johannes A. Gaus·Loris Schneider·Yitian Shi·Jongseok Lee·Rania Rayyes·Rudolph Triebel
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Abstract

In this paper, we address the point cloud registration problem, where well-known methods like ICP fail under uncertainty arising from sensor noise, pose-estimation errors, and partial overlap due to occlusion. We develop a novel approach, Gaussian Process Concept Attribution (GP-CA), which not only quantifies registration uncertainty but also explains it by attributing uncertainty to well-known sources of errors in registration problems. Our approach leverages active learning to discover new uncertainty sources in the wild by querying informative instances. We validate GP-CA on three publicly available datasets and in our real-world robot experiment. Extensive ablations substantiate our design choices. Our approach outperforms other state-of-the-art methods in terms of runtime, high sample-efficiency with active learning, and high accuracy. Our real-world experiment clearly demonstrates its applicability. Our video also demonstrates that GP-CA enables effective failure-recovery behaviors, yielding more robust robotic perception.

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BibTeXRIS

Johannes A. Gaus, Loris Schneider, Yitian Shi, Jongseok Lee, Rania Rayyes, Rudolph Triebel. 2025-09-23. Human-Interpretable Uncertainty Explanations for Point Cloud Registration. https://arxiv.org/abs/2509.18786

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