赤外・可視画像を用いた3D視触覚再構成によるロボットの微細触覚知覚
3D Vision-tactile Reconstruction from Infrared and Visible Images for Robotic Fine-grained Tactile Perception
人間の指先のような曲面視触覚センサを実現するため、プリズムと近赤外カメラで撮像系を拡張し、フォトメトリックステレオと境界制約付き法線積分を組み合わせて高精度な3D触覚形状再構成を可能にした。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yuankai Lin, Xiaofan Lu, Jiahui Chen, Hua Yang
分類: cs.RO
原文アブストラクト
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.
