基于通用光度立体的视觉触觉传感中无需校准的表面法线估计
Calibration-Free Surface Normals Estimation in Vision-Based Tactile Sensing using Universal Photometric Stereo
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中文总结 AI 辅助
提出无需校准的通用光度立体神经网络，从触觉图像估计接触表面法线，在多个传感器上验证，精度接近校准方法，奠定跨传感器触觉感知基础。
中文摘要 AI 辅助
视觉触觉传感器是捕获丰富接触表面几何形状的常用解决方案。然而，要获得高细节的接触表面法线和深度，必须通过将已知几何形状的探针物理按压到传感器弹性体上，并将触觉图像映射到真实探针形状上来校准传感器。这种方法无法在不同触觉传感器之间扩展，且校准工作可能因传感器形状和光学系统而复杂。相反，我们提出了一种使用通用光度立体神经网络进行接触表面法线估计的无校准程序。在一系列真实世界实验中，我们在3个具有不同光学系统的传感器上评估了我们的方法，证明通用方法是适合从触觉图像估计接触补丁处表面法线的途径，从而消除了触觉传感器校准的需要。使用金属球的对照实验表明，通用方法与校准方法匹配，平均角度误差为6.56°。我们展示了所提出的框架能够恢复具有自然纹理物体的高频表面细节，实现总体平均角度误差为10.66°。通用方法稳健地恢复了由穹顶形Digit 360捕获的接触表面法线，相对于校准基线实现了10.18°的低角度差异。该实验表明，在足够的照明设置下，可以使用仅由合成数据训练的模型来估计表面法线。通过在不同视觉触觉传感器设计之间提供接触表面的统一表示，通用光度立体神经网络为跨传感器的可迁移触觉感知奠定了基础。
英文摘要
Vision-based tactile sensors are a popular solution for capturing rich contact surface geometry. However, to obtain high-detail contact surface normals and depth, it is necessary to calibrate the sensor by physically pressing a probe with known geometry against the sensor elastomer and mapping tactile images onto the ground truth probe's shape. This approach does not scale across different tactile sensors, and the calibration effort can be complex depending on the sensor shape and optical system. Instead, we propose a calibration-free procedure for the estimation of contact surface normals using Universal Photometric Stereo neural networks. In a series of real-world experiments, we evaluate our approach on 3 sensors with different optical systems, demonstrating that universal methods are a suitable approach for estimating surface normals at the contact patch from tactile images, thereby alleviating the need for tactile sensor calibration. Controlled experiments with a metal ball show that universal methods match the calibrated method, with a mean angular error of $6.56^{\Large\circ}$. We show that the proposed framework recovers high-frequency surface details of objects with natural textures, achieving an overall mean angular error of $10.66^{\Large\circ}$. Universal method robustly recovers the contact surface normals captured with dome-shaped Digit 360, achieving a low angular discrepancy of $10.18^{\Large\circ}$ relative to the calibrated baseline. This experiment demonstrates that with sufficient illumination settings surface normals could be estimated using a model trained solely on synthetic data. By providing a unified representation of contact surfaces across different vision-based tactile sensor designs, Universal Photometric Stereo neural networks lay the foundation for transferable tactile perception across sensors.
发表机构
- TU Dresden（德累斯顿工业大学）
- TUD Dresden University of Technology（德累斯顿工业大学）
- Centre for Tactile Internet with Human-in-the-Loop (CeTI)（人在环触觉互联网中心（CeTI））
- DKFZ（德国癌症研究中心）
- University Hospital Carl Gustav Carus（卡尔·古斯塔夫·卡鲁斯大学医院）
- Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心（HZDR））
机构由 AI 辅助整理，请以论文原文为准。
