High-Speed Event Vision-Based Tactile Roller Sensor for Large Surface Measurements

Abstract-- Inspecting large-scale industrial surfaces like aircraft fuselages for quality control requires precise, high-resolution 3D geometry. Vision-based tactile sensors (VBTSs) offer high local resolution but require slow'press-and-lift' measurements for large areas. Sliding or roller/belt VBTS designs provide continuous measurement but face significant challenges: sliding suffers from friction/wear, while both are speed-limited by camera frame rates and motion blur . Thus, a rapid, continuous, high-resolution method is needed. We introduce a novel neuromorphic tactile roller sensor . It uses a modified event-based multi-view stereo algorithm for 3D reconstruction, leveraging high temporal resolution and motion blur robustness. This reconstruction is most effective for surfaces with distinct edges or sharp features, which are often the most critical for defect detection in industrial inspection tasks. We demonstrate 0.5 m/s scanning speeds with MAE below 100 µm (11x faster than prior methods). A multi-reference Bayesian fusion strategy reduces MAE by 25.2% (vs. Surface metrology and surface inspection are crucial elements in quality assurance across diverse industries, particularly aerospace and automotive manufacturing. Precise inspection is required to identify characteristics like paint quality, coating integrity, and subtle defects such as cracks, nicks, and dents [1], [2], [3]. Often, achieving a resolution of 0.1 mm or lower is necessary to accurately classify these features and ensure component integrity and safety [4]. Traditional contact-based methods, including high-precision profilometers [5], [6] or microscopic techniques [7], [8], [9], offer high resolution locally but become exceedingly time-consuming when applied to large surface areas due to their sequential, point-by-point or small-patch measurement nature. Non-contact optical methods, such as cameras, laser scanners, or structured light systems [2], [10], [11], [12], [13], [14], can significantly accelerate inspection by capturing data over wider areas. However, these methods often lack robustness; their performance can be compromised by variations in ambient lighting, motion blur when attempting high-speed scanning, or challenging surface optical properties like high reflectivity or transparency [15].