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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.19914 |
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| _version_ | 1866917319973797888 |
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| author | Khairi, Akram Sajwani, Hussain Alkilany, Abdallah Mohammad AbuAssi, Laith Halwani, Mohamad Zaid, Islam Mohamed Awadalla, Ahmed Swart, Dewald Ayyad, Abdulla Zweiri, Yahya |
| author_facet | Khairi, Akram Sajwani, Hussain Alkilany, Abdallah Mohammad AbuAssi, Laith Halwani, Mohamad Zaid, Islam Mohamed Awadalla, Ahmed Swart, Dewald Ayyad, Abdulla Zweiri, Yahya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | They See Me Rolling: High-Speed Event Vision-Based Tactile Roller Sensor for Large Surface Inspection Khairi, Akram Sajwani, Hussain Alkilany, Abdallah Mohammad AbuAssi, Laith Halwani, Mohamad Zaid, Islam Mohamed Awadalla, Ahmed Swart, Dewald Ayyad, Abdulla Zweiri, Yahya Robotics 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. |
| title | They See Me Rolling: High-Speed Event Vision-Based Tactile Roller Sensor for Large Surface Inspection |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.19914 |