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.

翻译：腐蚀作为一种导致金属材料劣化的自然过程，需要在工业场景中实施精密检测以实现质量控制并保护金属制品。传统腐蚀识别技术（包括超声检测、射线检测和漏磁检测）需在作业现场部署昂贵且笨重的设备进行数据采集，而采用轻量化常规相机系统与先进计算机视觉方法进行腐蚀识别则是一个尚未充分探索的替代方案。本文提出了一套用于工业环境中半自动化腐蚀识别与映射的完整系统。我们融合基于LiDAR的定位与建图技术最新进展，以及基于视觉的语义分割深度学习方法，构建工业环境的语义-几何地图。与现有文献中的腐蚀识别系统不同，本系统具有低成本、便携式、半自主化特征，且允许非专业人员采集大规模数据集。在室内实验室环境中的系列实验表明，所采用的基于LiDAR的三维建图与定位系统具有高精度，其平均绝对位姿误差和相对位姿误差分别小于0.05米和0.02米。此外，基于像素级人工标注数据集训练的数据驱动语义分割模型，其精确率达到约70%。