Inspecting large-scale industrial surfaces like aircraft fuselages for quality control requires capturing their precise 3D surface geometry at high resolution. Vision-based tactile sensors (VBTSs) offer high local resolution but require slow 'press-and-lift' measurements stitched for large areas. Approaches with sliding or roller/belt VBTS designs provide measurements continuity. However, they face significant challenges respectively: sliding struggles with friction/wear and both approaches are speed-limited by conventional camera frame rates and motion blur, making large-area scanning time consuming. Thus, a rapid, continuous, high-resolution method is needed. We introduce a novel tactile sensor integrating a neuromorphic camera in a rolling mechanism to achieve this. Leveraging its high temporal resolution and robustness to motion blur, our system uses a modified event-based multi-view stereo approach for 3D reconstruction. We demonstrate state-of-the-art scanning speeds up to 0.5 m/s, achieving Mean Absolute Error below 100 microns -- 11 times faster than prior continuous tactile sensing methods. A multi-reference Bayesian fusion strategy enhances accuracy (reducing MAE by 25.2\% compared to EMVS) and mitigates curvature errors. We also validate high-speed feature recognition via Braille reading 2.6 times faster than previous approaches.

翻译：在飞机机身等大规模工业表面进行质量控制检测时，需要以高分辨率捕获其精确的三维表面几何形状。基于视觉的触觉传感器（VBTSs）具有较高的局部分辨率，但需通过缓慢的“按压-抬起”测量方式拼接大面积区域。采用滑动或滚轮/皮带式VBTS设计的方案可实现连续测量，然而它们分别面临显著挑战：滑动方式受摩擦/磨损困扰，且两种方案均受限于传统相机帧率与运动模糊，导致大面积扫描耗时较长。因此，亟需一种快速、连续、高分辨率的测量方法。为此，我们提出一种集成神经形态相机于滚动机构中的新型触觉传感器。该系统利用神经形态相机的高时间分辨率与抗运动模糊特性，采用改进的基于事件的多视角立体视觉方法进行三维重建。实验表明，该系统实现了高达0.5米/秒的扫描速度（比现有连续触觉传感方法快11倍），平均绝对误差低于100微米。通过多参考贝叶斯融合策略，系统精度得到提升（与EMVS相比平均绝对误差降低25.2%），并有效抑制了曲率误差。此外，我们通过盲文识别验证了高速特征识别能力，其速度较先前方法提升2.6倍。