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

2025-07-26

AI Summary

To address the challenge of simultaneously achieving high spatial resolution and high-speed scanning in 3D geometric inspection of large industrial surfaces (e.g., aircraft fuselages), this paper proposes an event-based tactile rolling sensor. It introduces neuromorphic vision—specifically, an event camera—into a rolling tactile sensing system for the first time, integrating event-driven multi-view stereo matching with a multi-reference Bayesian fusion algorithm to overcome conventional frame-rate limitations and suppress motion blur. The method enables continuous scanning at 0.5 m/s, achieving absolute 3D reconstruction errors below 100 μm. Compared to state-of-the-art continuous tactile approaches, it improves scanning speed by 11× and Braille recognition speed by 2.6×. Key contributions include the deep integration of event cameras with rolling tactile sensing, and a real-time, high-precision geometric reconstruction framework tailored for dynamically curved surfaces.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• High-resolution 3D surface measurement for large industrial areas
• Overcoming speed limitations of conventional tactile sensors
• Reducing motion blur and friction in continuous tactile scanning

Innovation

• Neuromorphic camera in rolling mechanism
• Event-based multi-view stereo reconstruction
• Multi-reference Bayesian fusion strategy