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Semantic mapping is the task of providing a robot with a map of itsenvironment beyond the open, navigable space of traditional SimultaneousLocalization and Mapping (SLAM) algorithms by attaching semantics to locations.The system presented in this work reads door placards to annotate the locationsof offices. Whereas prior work on this system developed hand-crafted detectors,this system leverages YOLOv5 for sign detection and EAST for text recognition.Placards are localized by computing their pose from a point cloud in a RGB-Dcamera frame localized by a modified ORB-SLAM. Semantic mapping is accomplishedin a post-processing step after robot exploration from video recording. Systemperformance is reported in terms of the number of placards identified, theaccuracy of their placement onto a SLAM map, the accuracy of the map built, andthe correctness transcribed placard text.

Automatic Sign Reading and Localization for Semantic Mapping with an Office Robot