Abstract
This paper deals with the application of computer vision to mining automation. In particular, it presents results from an on-going research project investigating the automation of the rock-breaking process, a process typical of underground hardrock mining. The objective of this research is to investigate the use of computer vision to identify and locate the oversized lumps remaining on a grizzly (metal sieve) after blasting, for the secondary rock-breaking operation. Range images of rocks are acquired in our laboratory using the NRCC/McGill laser rangefinder. Rock-pile images are segmented into parts (rocks) based on their surface characteristics. Finally, superquadric models are fit to the segmented range data to characterize the 3D shape of each rock.
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