Efficient Placard Discovery for Semantic Mapping During Frontier Exploration
This work addresses the need for robots to autonomously annotate environments with semantic information, though it is incremental as it builds on existing detection and exploration techniques.
The paper tackles the problem of autonomous semantic mapping by enabling a robot to discover and read door placards during exploration, resulting in significantly faster autonomous discovery compared to previous methods.
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.