ICRA 2011 Paper Abstract


Paper TuP112.5

Silversides, Katherine (The University of Sydney), Melkumyan, Arman (Australian Centre for Field Robotics, The University of Sydney), Wyman, Derek (The University of Sydney), Hatherly, Peter (University of Sydney), Nettleton, Eric (The University of Sydney)

Detection of Geological Structure Using Gamma Logs for Autonomous Mining

Scheduled for presentation during the Regular Sessions "Field and Underwater Robotics I" (TuP112), Tuesday, May 10, 2011, 14:40−14:55, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Mining Robotics, Mapping, Field Robots


This work is motivated by the need to develop new perception and modelling capabilities to support a fully autonomous, remotely operated mine. The application differs from most existing robotics research in that it requires a detailed world model of the sub-surface geological structure. This in-ground geological information is then used to drive many of the planning and control decisions made on a mine site. This paper formulates a method for automatically detecting in-ground geological boundaries using geophysical logging sensors and a supervised learning algorithm. The algorithm uses Gaussian Processes (GPs) and a single length scale squared exponential covariance function. The approach is demonstrated on data from a producing iron-ore mine in Australia. Our results show that two separate distinctive geological boundaries can be automatically identified with an accuracy of over 99 percent. The alternative approach to automatic detection involves manual examination of these data.



Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2020 PaperCept, Inc.
Page generated 2020-03-30  01:12:46 PST  Terms of use