Efficient and accurate 3D object shape reconstruction contributes significantly to the success of a robot's physical interaction with its environment. Acquiring accurate shape information about unknown objects is challenging, especially in unstructured environments, e.g. the vision sensors may only be able to provide a partial view. To address this issue, tactile sensors could be employed to extract local surface information for more robust unknown object shape estimation. In this paper, we propose a novel approach for efficient unknown 3D object shape exploration and reconstruction using a multi-fingered hand equipped with tactile sensors and a depth camera only providing a partial view. We present a multi-finger sliding touch strategy for efficient shape exploration using a Bayesian Optimization approach and a single-leader-multi-follower strategy for multi-finger smooth local surface perception. We evaluate our proposed method by estimating the 3D shape of objects from the YCB and OCRTOC datasets based on simulation and real robot experiments. The proposed approach yields successful reconstruction results relying on only a few continuous sliding touches. Experimental results demonstrate that our method is able to model unknown objects in an efficient and accurate way.

翻译：高效且准确的三维物体形状重建对机器人成功与环境进行物理交互至关重要。获取未知物体的精确形状信息具有挑战性，尤其在非结构化环境中，例如视觉传感器可能仅能提供部分视角。为解决此问题，可利用触觉传感器提取局部表面信息，以更鲁棒地估计未知物体形状。本文提出一种新颖方法，通过配备触觉传感器且仅能获取部分视角深度相机的多指手，实现高效未知三维物体形状的探索与重建。我们提出了一种多指滑触策略，结合贝叶斯优化方法实现高效形状探索，并采用单领导者-多跟随者策略实现多指平滑局部表面感知。基于YCB和OCRTOC数据集，通过仿真与真实机器人实验评估所提方法，验证其仅需少量连续滑触即可成功重建物体。实验结果表明，该方法能够以高效且准确的方式对未知物体进行建模。