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

Source: [A Complete System for Automated 3D Semantic-Geometric Mapping of Corrosion in Industrial Environments](<https://www.sota2.com/research/paper/68fa72fa1e209131cea59b86>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:39:34.909Z

Authors: Rui Pimentel de Figueiredo, Stefan Nordborg Eriksen, Ignacio Rodriguez, Simon B{\\o}gh

Published: 2024-04-21T15:40:32.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.13691>)

[PDF](<https://arxiv.org/pdf/2404.13691.pdf>)