{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/106400"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/106400","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Point cloud segmentation for mobile robot manipulation","abstract":"In this thesis, we develop a system for estimating a belief state for a scene over multiple observations of the scene. Given as input a sequence of observed RGB-D point clouds of a scene, a list of known objects in the scene and their pose distributions as a prior, and a black-box object detector, our system outputs a belief state of what is believed to be in the scene. This belief state consists of the states of known objects, walls, the floor, and \"stuff\" in the scene based on the observed point clouds. 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