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Paper WeC07.1

Schneider, Sven (Australian Centre for Field Robotics, The University ofSydney), Melkumyan, Arman (The University of Sydney), Murphy, Richard (Australian Centre for Field Robotics, The University of Sydney), Nettleton, Eric (The University of Sydney)

A Geological Perception System for Autonomous Mining

Scheduled for presentation during the Regular Session "Environment Mapping" (WeC07), Wednesday, May 16, 2012, 14:30−14:45, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 24, 2017

Keywords Sensor Fusion, Mining Robotics, Mapping

Abstract

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.

 

 

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