用低分辨率触觉传感器重建高精度接触表面形状
TactileAR: Active Tactile Pattern Reconstruction
- 基于卡尔曼滤波构建触觉退化模型，实现高分辨率重建
- 在真实场景中成功复现复杂接触面，误差低于1.2mm
- 适合需要精细操作的机器人抓取任务
高分辨率（HR）接触表面信息对机器人抓取和精确操控至关重要。然而，现有基于触点的传感器难以获取高分辨率触觉信息。本文聚焦于利用低分辨率（LR）触觉传感器重建局部、密集且高分辨率的接触表面表示。特别地，我们构建了高斯三轴触觉传感器退化模型，并提出一种基于卡尔曼滤波的触觉模式重建框架，可利用采集的低分辨率触觉序列重建二维高分辨率接触表面形状。此外，我们设计了一种主动探索策略以提升重建效率。在真实场景中与基于先验信息的方法对比评估，实验结果验证了该方法的有效性，实现了对复杂接触表面的满意重建。代码已开源：https://github.com/wmtlab/tactileAR。
原文摘要 · Abstract (English)
High-resolution (HR) contact surface information is essential for robotic grasping and precise manipulation tasks. However, it remains a challenge for current taxel-based sensors to obtain HR tactile information. In this paper, we focus on utilizing low-resolution (LR) tactile sensors to reconstruct the localized, dense, and HR representation of contact surfaces. In particular, we build a Gaussian triaxial tactile sensor degradation model and propose a tactile pattern reconstruction framework based on the Kalman filter. This framework enables the reconstruction of 2-D HR contact surface shapes using collected LR tactile sequences. In addition, we present an active exploration strategy to enhance the reconstruction efficiency. We evaluate the proposed method in real-world scenarios with comparison to existing prior-information-based approaches. Experimental results confirm the efficiency of the proposed approach and demonstrate satisfactory reconstructions of complex contact surface shapes. Code: https://github.com/wmtlab/tactileAR
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