Large Scale Terrain Modelling for Autonomous Mining

This thesis is concerned with development of a terrain model using Gaussian Processes to support the automation of open-pit mines. Information can be provided from a variety of sources including GPS, laser scans and manual surveys. The information is then fused together into a single representation of the terrain together with a measure of uncertainty of the estimated model. The model is also used to detect and label specific features in the terrain. In the context of mining, theses features are edges known as toes and crests. A combination of clustering and classification using supervised learning detects and labels these regions. Data gathered from production iron ore mines in Western Australia and a farm in Marulan outside Sydney is used to demonstrate and verify the ability of Gaussian Processes to estimate a model of the terrain. The estimated terrain model is then used for detecting features of interest. Results show that the Gaussian Process correctly estimates the terrain and uncertainties, and provide a good representation of the area. Toes and crests are also successfully identified and labelled.