This article presents a novel multifidelity-based optimal sampling method to rapidly estimate the shape of an object from the touch-down points on its surface given a highly limited number of sampling trials. The proposed approach attempts to improve the existing shape estimation via tactile exploration, which uses Gaussian process regression for implicit surface modeling with sequential sampling. The main objective is to make the process of sample point selection more efficient and systematic such that the unknown shape can be estimated fast and accurately with highly limited sample points (e.g., less than 1% of the original dataset). Specifically, we propose to select the next best sample point based on two optimization criteria: 1) the mutual information (MI) for uncertainty reduction, and 2) the local curvature for fidelity enhancement. The combination of these two objectives leads to an optimal sampling process that balances between the exploration of the whole shape and the exploitation of the local area where the higher fidelity (or more sampling) is required. Simulation and experimental results successfully demonstrate the advantage of the proposed method in terms of estimation speed and accuracy over the conventional methods. Our approach allows us to reconstruct recognizable three dimensional shapes using only around optimally selected 0.4% of the original dataset.

中文翻译：

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通过触觉探索进行快速贝叶斯形状估计的多保真度采样


本文提出了一种新颖的基于多保真度的最佳采样方法，可以在采样试验次数非常有限的情况下，从物体表面的着陆点快速估计物体的形状。所提出的方法试图通过触觉探索来改进现有的形状估计，该方法使用高斯过程回归通过顺序采样进行隐式表面建模。主要目标是使样本点选择过程更加高效和系统，以便可以利用高度有限的样本点（例如，小于原始数据集的1%）快速准确地估计未知形状。具体来说，我们建议根据两个优化标准选择下一个最佳样本点：1）用于减少不确定性的互信息（MI），2）用于增强保真度的局部曲率。这两个目标的结合产生了一个最佳采样过程，该过程在整个形状的探索和需要更高保真度（或更多采样）的局部区域的开发之间取得平衡。仿真和实验结果成功证明了该方法在估计速度和精度方面相对于传统方法的优势。我们的方法允许我们仅使用原始数据集的最佳选择的 0.4% 来重建可识别的三维形状。