They See Me Rolling: High-Speed Event Vision-Based Tactile Roller Sensor for Large Surface Inspection

TL;DR

This paper presents a high-speed, high-resolution tactile sensor using a neuromorphic camera in a rolling mechanism, enabling rapid large-area surface inspection with improved accuracy and speed over existing methods.

Contribution

Introduction of a novel neuromorphic camera-based rolling tactile sensor with a multi-view stereo approach for fast, accurate 3D surface reconstruction in industrial inspection.

Findings

Achieves scanning speeds up to 0.5 m/s, 11 times faster than previous methods.

Reduces mean absolute error below 100 microns, improving accuracy.

Demonstrates high-speed Braille reading 2.6 times faster than prior approaches.

Abstract

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.