  Semantic mapping is the task of providing a robot with a map of its
environment beyond the open, navigable space of traditional Simultaneous
Localization and Mapping (SLAM) algorithms by attaching semantics to locations.
The system presented in this work reads door placards to annotate the locations
of 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-D
camera frame localized by a modified ORB-SLAM. Semantic mapping is accomplished
in a post-processing step after robot exploration from video recording. System
performance is reported in terms of the number of placards identified, the
accuracy of their placement onto a SLAM map, the accuracy of the map built, and
the correctness transcribed placard text.
