A Complete System for Automated 3D Semantic-Geometric Mapping of Corrosion in Industrial Environments
<|reference_start|>A Complete System for Automated 3D Semantic-Geometric Mapping of Corrosion in Industrial Environments: 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.<|reference_end|>
@article{de figueiredo2024a,
    title={A Complete System for Automated 3D Semantic-Geometric Mapping of
  Corrosion in Industrial Environments},
    author={Rui Pimentel de Figueiredo, Stefan Nordborg Eriksen, Ignacio
  Rodriguez, Simon B{o}gh},
    journal={arXiv preprint arXiv:2404.13691},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13691},
    primaryClass={cs.CV cs.RO}
}