Many robotics applications involve navigating to positions specified in terms of their semantic significance. A robot operating in a hotel may need to deliver room service to a named room. In a hospital, it may need to deliver medication to a patient's room. The Building-Wide Intelligence Project at UT Austin has been developing a fleet of autonomous mobile robots, called BWIBots, which perform tasks in the computer science department. Tasks include guiding a person, delivering a message, or bringing an object to a location such as an office, lecture hall, or classroom. The process of constructing a map that a robot can use for navigation has been simplified by modern SLAM algorithms. The attachment of semantics to map data, however, remains a tedious manual process of labeling locations in otherwise automatically generated maps. This paper introduces a system called PRISM to automate a step in this process by enabling a robot to localize door signs -- a semantic markup intended to aid the human occupants of a building -- and to annotate these locations in its map.

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In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 2018.

@inproceedings{IROS18-hart, title={PRISM: Pose Registration for Integrated Semantic Mapping}, author={Justin W. Hart and Rishi Shah and Sean Kirmani and Nick Walker and Kathryn Baldauf and Nathan John and Peter Stone}, booktitle={Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, month={October}, address={Madrid, Spain}, url="http://www.cs.utexas.edu/users/ai-lab?IROS18-hart", year={2018} }