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

TL;DR

This paper presents an automated system for reading and localizing office door signs to enhance semantic mapping in robots, utilizing deep learning for sign detection and text recognition, and integrating pose estimation for accurate placement.

Contribution

It introduces a novel approach combining YOLOv5 and EAST with pose estimation for semantic mapping, improving automation and accuracy over prior hand-crafted detector methods.

Findings

High accuracy in placard detection and localization

Improved semantic map correctness and completeness

Effective integration of deep learning with SLAM for semantic annotation

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 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.