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ORIGINAL RESEARCH article

Front. Robot. AI
Sec. Field Robotics
Volume 11 - 2024 | doi: 10.3389/frobt.2024.1424883
This article is part of the Research Topic Semantic SLAM for Mobile Robot Navigation View all articles

Visual Place Recognition from End-to-End Semantic Scene Text Features

Provisionally accepted
John Zelek John Zelek 1*Zobeir Raisi Zobeir Raisi 2
  • 1 University of Waterloo, Waterloo, Ontario, Canada
  • 2 Chabahar Maritime University, Chabahar, Sistan and Baluchestan, Iran

The final, formatted version of the article will be published soon.

    We live in a visual world where text cues are abundant in urban environments. The premise for our work is for robots to capitalize on these text features for visual place recognition. A new technique is introduced that uses an end-to-end scene text detection and recognition technique to improve robot localization and mapping through Visual Place Recognition (VPR). This technique addresses several challenges such as arbitrary shaped text, illumination variation, and occlusion.The proposed model captures text strings and associated bounding boxes specifically designed for VPR tasks. The primary contribution of this work is the utilization of an end-to-end scene text spotting framework that can effectively capture irregular and occluded text in diverse environments.We conduct experimental evaluations on the Self-Collected Text Place (SCTP) benchmark dataset, and our approach outperforms state-of-the-art methods in terms of precision and recall, which validates the effectiveness and potential of our proposed approach for VPR.

    Keywords: robot, localization, Scene Text Detection, Scene text recognition, Scene text spotting, Visual place recognition

    Received: 28 Apr 2024; Accepted: 29 Aug 2024.

    Copyright: © 2024 Zelek and Raisi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: John Zelek, University of Waterloo, Waterloo, N2L 3G1, Ontario, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.