用红外可见光融合重建3D触觉，提升机械手精细感知能力
3D Vision-tactile Reconstruction from Infrared and Visible Images for Robotic Fine-grained Tactile Perception
- 通过棱镜与近红外相机扩展成像通道，实现多视角图像采集
- 正常估计准确率提升40%，显著改善曲面触觉重建精度
- 适合需要高精度触觉反馈的机器人抓取与操作任务
为实现类人形夹持器的人类级触觉感知，视觉触觉传感器（VTS）的柔性传感表面需从传统平面结构演进为具有连续表面梯度的仿生曲面。然而，将平面VTS扩展至曲面时面临光照不足、重建模糊及表面结构空间边界条件复杂等挑战。为此，本研究（i）开发GelSplitter3D，通过棱镜与近红外（NIR）相机扩展成像通道；（ii）提出基于CAD生成法向真值的光度立体神经网络，校准触觉几何；（iii）设计结合深度先验边界约束的法向积分方法，纠正表面积分累积误差。实验表明，该方法在触觉感知性能上表现更优，法向估计准确率提升40%，且传感器形态在抓取与操作任务中展现出明显优势。
原文摘要 · Abstract (English)
To achieve human-like haptic perception in anthropomorphic grippers, the compliant sensing surfaces of vision tactile sensor (VTS) must evolve from conventional planar configurations to biomimetically curved topographies with continuous surface gradients. However, planar VTSs have challenges when extended to curved surfaces, including insufficient lighting of surfaces, blurring in reconstruction, and complex spatial boundary conditions for surface structures. With an end goal of constructing a human-like fingertip, our research (i) develops GelSplitter3D by expanding imaging channels with a prism and a near-infrared (NIR) camera, (ii) proposes a photometric stereo neural network with a CAD-based normal ground truth generation method to calibrate tactile geometry, and (iii) devises a normal integration method with boundary constraints of depth prior information to correcting the cumulative error of surface integrals. We demonstrate better tactile sensing performance, a 40$\%$ improvement in normal estimation accuracy, and the benefits of sensor shapes in grasping and manipulation tasks.
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