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dc.contributor.advisorHutchinson, Seth
dc.contributor.authorChen, Kevin Julian
dc.date.accessioned2020-11-09T16:59:07Z
dc.date.available2020-11-09T16:59:07Z
dc.date.created2019-12
dc.date.submittedDecember 2019
dc.identifier.urihttp://hdl.handle.net/1853/63848
dc.description.abstractRich, yet efficient knowledge processing is one of the key problems in modern autonomous robotics. The Robot Autonomy and Interactive Learning (RAIL) Lab at the Georgia Institute of Technology has developed a new knowledge processing framework named Robot Common Sense Embedding (RoboCSE), which leverages multi-relational embeddings to learn object affordances, locations, and materials. This project aims to test the capabilities of RoboCSE for household robots by building a perception pipeline, which outputs a semantic map (i.e. map with object labels). The perception pipeline consists of two main components: a Simultaneous Localization and Mapping (SLAM) algorithm to build an occupancy map and two object classifiers to label objects in the map. We hope to integrate the semantic map into RoboCSE to test RoboCSE’s ability to perform high-level task planning and knowledge sharing.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectRobotics
dc.subjectSLAM
dc.subjectObject recognition
dc.subjectKnowledge reasoning
dc.titleSemantic Mapping and Reasoning
dc.typeUndergraduate Research Option Thesis
dc.description.degreeUndergraduate
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science
thesis.degree.levelUndergraduate
dc.date.updated2020-11-09T16:59:07Z


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