用可解释AI分析点云配准不确定性来源，让机器人知道为何失败。
Towards Explaining Uncertainty Estimates in Point Cloud Registration
- 基于核SHAP方法解析概率ICP的输出成因
- 识别传感器噪声、遮挡和环境模糊等关键不确定因素
- 适合关注机器人可靠性与可解释性的研究者
迭代最近点（ICP）是用于估计两组点云间变换的常用算法。本文旨在利用可解释AI的最新进展，对提供不确定性估计的概率ICP方法进行解释。我们提出一种方法，能说明概率ICP为何产生特定输出。该方法基于核SHAP（SHapley Additive exPlanations），为传感器噪声、遮挡及环境模糊等常见不确定性来源分配重要性值。实验结果表明，该解释方法能合理揭示不确定性来源，朝着让机器人以人类可理解的方式知晓自身失败原因迈出一步。
原文摘要 · Abstract (English)
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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