A Complete System for Automated 3D Semantic-Geometric Mapping of Corrosion in Industrial Environments

• URL: http://arxiv.org/abs/2404.13691v1
• Date: Sun, 21 Apr 2024 15:40:32 GMT
• Title: A Complete System for Automated 3D Semantic-Geometric Mapping of Corrosion in Industrial Environments
• Authors: Rui Pimentel de Figueiredo, Stefan Nordborg Eriksen, Ignacio Rodriguez, Simon Bøgh,
• Abstract summary: We propose a complete system for semi-automated corrosion identification and mapping in industrial environments. We leverage recent advances in LiDAR-based methods for localization and mapping, with vision-based semantic segmentation deep learning techniques. A set of experiments in an indoor laboratory environment, demonstrate quantitatively the high accuracy of the employed LiDAR based 3D mapping and localization system.
• Score: 0.6749750044497731
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional techniques for corrosion identification, including ultrasonic testing, radio-graphic testing, and magnetic flux leakage, necessitate the deployment of expensive and bulky equipment on-site for effective data acquisition. An unexplored alternative involves employing lightweight, conventional camera systems, and state-of-the-art computer vision methods for its identification. In this work, we propose a complete system for semi-automated corrosion identification and mapping in industrial environments. We leverage recent advances in LiDAR-based methods for localization and mapping, with vision-based semantic segmentation deep learning techniques, in order to build semantic-geometric maps of industrial environments. Unlike previous corrosion identification systems available in the literature, our designed multi-modal system is low-cost, portable, semi-autonomous and allows collecting large datasets by untrained personnel. A set of experiments in an indoor laboratory environment, demonstrate quantitatively the high accuracy of the employed LiDAR based 3D mapping and localization system, with less then $0.05m$ and 0.02m average absolute and relative pose errors. Also, our data-driven semantic segmentation model, achieves around 70\% precision when trained with our pixel-wise manually annotated dataset.

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