Detection of Geological eological Structure using tructure Gamma amma Logs for ogs Autonomous Mining
This work is motivated by the need to develop new perception and modeling 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 sub-surface geological structure. This in in-ground geological information is t then used to drive many of the hen planning and control decisions made on a mine site. This paper formulates a method for automatically detecting in in-ground geological boundaries using geophysical logging sensors and a supervised learning algorithm. The algorit algorithm uses Gaussian Processes (GPs) hm and a single length scale squared exponential covariance function. The approach is demonstrated on data from a produc producing ing iron iron-ore mine in Australia. Our results show that two separate distinctive geological boundaries can be automatically identified with an accuracy of over 99 e percent. The alternative approach to automatic detection involves manual examination of these data.
Katherine L. Silversides Arman Melkumyan Derek A. Wyman Peter J. Hatherly Eric Nettleton
Australian Centre for Field Robotics,University of Sydney Sydney,NSW 2006,Australia School of Geosc Australian Centre for Field Robotics,University of Sydney Sydney,NSW 2006,Australia School of Geosciences,University of Sydney Sydney,NSW 2006,Australia
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
1577-1582
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）