EGU23-13899, updated on 10 Jan 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Robot-aided autonomous hyperspectral mapping in mining environments

Sandra Lorenz, Moritz Kirsch, Margret Fuchs, Sam Thiele, and Richard Gloaguen
Sandra Lorenz et al.
  • Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, Department of Exploration, Freiberg, Germany (

Geological face mapping is a frequently recurring task in mining operations, the results of which have an immediate influence on the mines’ profitability, safety, and environmental impact. Hyperspectral imaging is an increasingly applied technology to improve the efficiency and accuracy of mapping tasks. The rapid and non-destructive acquisition of spectral material properties allows meaningful material information such as mineralogical surface composition to be obtained in a safe and efficient manner. The fusion product of backprojected hyperspectral data with 3D surface information (so-called “hyperclouds”) further enhances the data value by enabling easier data correction, integration, and implementation into digital archives and models. Mining environments, however, remain a challenge for operational hyperspectral mapping, particularly underground where inadequate lighting, access, and safety of operation make data collection difficult. Data processing and interpretation require expert knowledge and are typically performed semi-manually and offline. To be economically viable in such mining environments, the hypercloud technology has to mature toward autonomy and real-time delivery of results. In recent years, terrestrial autonomous platforms have entered the market that are suited to the challenging conditions of underground mining and can maneuver and navigate even in confined, uneven, and poorly lit environments. They provide optimal carriers for hyperspectral sensors, which have simultaneously evolved into lighter, faster, and more robust devices. However, implementing hyperspectral sensors as payload for terrestrial autonomous robots remains challenging, especially in terms of  technical compatibility, ensuring data quality under complex conditions,  and processing large amounts of data quickly and autonomously. In our contribution, we demonstrate the potential of autonomous terrestrial robots combined with hyperspectral technology and advanced data processing for the automation of geological mapping. We present results of hyperspectral data acquisition using an autonomous robotic platform in a confined underground mining environment and discuss strategies for adapted sensor design, autonomous validation, real-time hypercloud processing, and enhanced autonomous navigation supported by hyperspectral information. 

How to cite: Lorenz, S., Kirsch, M., Fuchs, M., Thiele, S., and Gloaguen, R.: Robot-aided autonomous hyperspectral mapping in mining environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13899,, 2023.