Robot-aided Hyperspectral Imaging For Mineral Exploration in Underground Mining Environments

Master thesis (2023)

Authors

S. Dieters Civil Engineering & Geosciences

Contributors

T. Schmiedel Resource Engineering - CEG (mentor)
R.C. Lindenbergh Optical and Laser Remote Sensing - CEG (graduation committee member)
Sandra Lorenz Helmholtz Institute Freiberg for Resource Technology (coach)
Moritz Kirsch Helmholtz Institute Freiberg for Resource Technology (coach)
Bernd Lottermoser Rheinisch-Westfälische Technische Hochschule (graduation committee member)
Jussi Leveinen Aalto University (graduation committee member)
Faculty
Civil Engineering & Geosciences, Civil Engineering & Geosciences
Copyright: © 2023 Sibren Dieters
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Published Date

02-11-2023

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

New mining and exploration projects have revived across Europe with an increasing demand for critical raw materials driven by the energy transition and unstable political conditions. Hyperspectral imaging proves to be an excellent tool for quick and efficient non-invasive mineral exploration. By mounting hyperspectral- and LiDAR sensors on drones and other robots, inaccessible and dangerous areas can safely be mapped for geological information. With increasingly deeper and more hazardous underground mines, a robot-aided
hyperspectral mineral exploration method in underground environments is needed. This study configures a Cubert X20P hyperspectral camera (VNIR) and VLP16 LiDAR on the versatile Boston Dynamics Spot robot, creating a multi-sensor robotic platform for data acquisition in underground mining environments. A data workflow is proposed and applied to the granite greisen rocks of the Zinnwald/Cinnovec mine (Germany). Combining hyperspectral and geometric data provides unique 3D hypercloud results interpreted for mineral
and structural features. Hyperspectral analysis successfully identifies iron and clay minerals along with multiple vein and fault structures. A remote mineral exploration method in underground mines significantly improves safety by keeping the operator away from hazardous areas. The proposed platform and workflow show potential to contribute to underground mineral exploration, especially if future improvements in data quality and autonomous capabilities are made.