  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 YOLOv2 for detection and a segmentation network for
segmentation. Placards are localized by computing their pose from a homography
computed from a segmented quadrilateral outline. This work also introduces an
Interruptable Frontier Exploration algorithm, enabling the robot to explore its
environment to construct its SLAM map while pausing to inspect placards
observed during this process. This allows the robot to autonomously discover
room placards without human intervention while speeding up significantly over
previous autonomous exploration methods.
