{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/10492"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/10492","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Drivable region detection for autonomous robots applied to South African underground mining","abstract":"This dissertation focuses on enhancing autonomous robots' capability to identify drivable regions in underground terrains. A system model that compares the drivability analysis of underground terrains using the entropy model and statistical region merging (SRM) was developed, with a view to presenting an analysis of 2D and 3D results. The approach involves standard image-processing techniques, such as colour and texture feature extraction and region segmentation for underground image classification. A probabilistic method based on the local entropy was employed. 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A system model that compares the drivability analysis of underground terrains using the entropy model and statistical region merging (SRM) was developed, with a view to presenting an analysis of 2D and 3D results. The approach involves standard image-processing techniques, such as colour and texture feature extraction and region segmentation for underground image classification. A probabilistic method based on the local entropy was employed. The entropy is measured within a fixed window on each frame in order to compute features used in the segmentation process. This research compares the results obtained from the entropy method and SRM approach. 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