Perception for a roadheader in automatic selective cutting operation
Résumé
The authors show how color and texture image segmentation, automatic image classification, camera calibration, and 3D scene representation can cooperate to solve a complex problem such as selective cutting. Attention is focused on the field of ore recognition, where significant improvement has been obtained by texture information. The research project considered involves the automation of the cutting operation of a roadheader for selective cutting in an underground potash mine near Barcelona in Spain. The system described is based on the use of computer vision to discriminate the different ore types found in the face (sylvinite, carnalite, and salt). Using the information about the ore distribution, paths are then planned for the computer-controlled cutting boom. It was shown that color information was important for minerals identification but texture information was absolutely necessary to get a good identification in all cases. The two kinds of information cooperate in an automatic image classification algorithm, which has been validated on many images of the mine face.