Efficient Placard Discovery for Semantic Mapping During Frontier Exploration

TL;DR

This paper introduces an efficient method for semantic mapping that uses YOLOv2 for placard detection and an interruptible exploration algorithm, enabling autonomous discovery of room labels during exploration.

Contribution

It presents a novel approach combining deep learning detection with an interruptible exploration strategy for improved autonomous semantic mapping.

Findings

Significantly faster exploration compared to previous methods.

Successful autonomous detection and localization of room placards.

Enhanced semantic mapping capabilities during frontier exploration.

Abstract

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.