A geological perception system for autonomous mining

There is a strong push within the mining sector to develop and adopt automation technology, including autonomous vehicles such as excavators, trucks and drills. However, for autonomous systems to operate effectively in this domain, new perception capabilities are required to build rich models of a mine. A key element of this is an ability to sense and model the sub-surface geological structure as well as the more traditional robotic models, which typically estimate terrain and obstacles. This paper presents a new automated geological perception system to support autonomous mining. It uses hyperspectral imaging sensors and a supervised learning algorithm to detect and classify geological structures, and ultimately build a rich model of the operating environment. The presented algorithm uses Gaussian Processes (GPs) and an Observation Angle Dependent (OAD) covariance function. Further, the resulting geological model can be improved by fusing data from two hyperspectral scanners which measure different regions of the spectrum. The approach is demonstrated using data from an operational iron-ore mine. Fusion of classification results from the two sensors shows better agreement with ground truth mapping done in the field, compared to results from individual sensors.

Authors

• Sven Schneider 0003
• Arman Melkumyan
• Richard J. Murphy
• Eric Nettleton

Keywords

• Sensors
• Rocks
• Hyperspectral imaging
• Fuel processing industries
• Face
• Perceptual System
• Geological Systems
• Autonomous Mining
• Classification Results
• Autonomic System
• Gaussian Process
• Autonomous Vehicles
• Covariance Function
• Geological Structures
• Hyperspectral Sensors
• Individual Sensors
• Ground Truth Map
• Raw Data
• Spatial Resolution
• Training Set
• Spectral Resolution
• Class Labels
• Process Mining
• Spectral Library
• Data Fusion
• Visible Near-infrared
• Shortwave Infrared
• Side Of The Image
• Rock Types
• Hyperspectral Data
• Open-pit
• Inertial Navigation
• Hyperspectral Imagery
• Probabilistic Framework
• High Spectral Resolution