{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/39260"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/39260","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Towards a Visual Simultaneous Localization and Mapping System for Computationally Constrained Systems","abstract":"Future robotic planetary exploration missions such as sample retrieval and in-situ resource utilization will require more accurate localization techniques such as Simultaneous Localization and Mapping (SLAM) to achieve the science goals. In this thesis, a visual SLAM system aimed towards computationally constrained systems is presented. In this work, Binary Robust Invariant Scalable Keypoints (BRISK) and Oriented FAST and Rotated BRIEF (ORB) feature descriptors are introduced and compared against Speeded Up Robust Features (SURF). 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