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Mining haul truck pose estimation and load profiling using stereo vision

University of British Columbia

Earthmoving at surface mines centers around very large excavators (mining shovels) that remove material from the earth and place it into haul trucks. During this loading process, the truck may be inadvertently loaded in a manner that injures the truck driver, or that results in an asymmetrically loaded or overloaded truck. This thesis presents two systems which aim to assist with haul truck loading: 1) a stereo-vision based system that determines a haul truck's pose relative to the shovel housing as part of an operator loading assistance system, and 2) a system that can determine a haul truck's load volume and distribution as the truck is being loaded. The haul truck pose estimation system is significant in that it is the first six-degrees of freedom truck pose estimation system that is sufficiently fast and accurate to be applicable in an industrial mine setting. Likewise, it is the first time that a system capable of determining a haul truck's volume as it is being loaded has been described. To achieve this, a fast, resolution independent nearest neighbour search is presented and used within Iterative Closest Point (ICP) for point cloud registration. It also shown, for the first time, to the best of our knowledge, the possibility of using the Perspective-n-Point (PnP) pose estimation technique to estimate the pose a range-sensor derived point cloud model, and to use the same technique to verify the pose given by ICP. Camera errors, registration errors, pose estimation errors, volume estimation errors and computation times are all reported.

Text

2010-02-28

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/21423

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/