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Vision-Based Localization in Urban Areas for Mobile Robots.

Erdal Alimovski1, Gokhan Erdemir2, Ahmet Emin Kuzucuoglu3

  • 1Computer Engineering Department, Istanbul Sabahattin Zaim University, 34303 Istanbul, Türkiye.

Sensors (Basel, Switzerland)
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PubMed
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This summary is machine-generated.

This study introduces a visual localization system for outdoor robots in cities, outperforming GPS in areas with signal issues. The system uses text recognition to achieve accurate robot positioning with minimal error.

Keywords:
mapmobile robottext recognitionvisual localization

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Area of Science:

  • Robotics and Computer Vision
  • Autonomous Navigation Systems
  • Geospatial Localization Technologies

Background:

  • Robust autonomous navigation relies on accurate localization, a critical factor for mobile robots.
  • Global Positioning Systems (GPS) are standard for outdoor localization but suffer from signal degradation in urban environments due to obstructions.
  • This limitation necessitates alternative localization methods for reliable operation in challenging urban settings.

Purpose of the Study:

  • To propose and evaluate a visual-based localization system for outdoor mobile robots operating in crowded urban environments.
  • To address the limitations of GPS in areas with signal blockage or reduced precision.
  • To enhance the accuracy and reliability of mobile robot localization using readily available visual data.

Main Methods:

  • Utilized the Efficient and Accurate Scene Text Detector (EAST) algorithm for urban text detection.
  • Employed EasyOCR for text recognition, followed by post-processing and word similarity algorithms to refine extracted information.
  • Integrated recognized text data with the Places API to obtain geographical coordinates and collected parallel Points of Interest (POI) data.

Main Results:

  • The proposed visual localization system demonstrated a low localization error of approximately 4 meters in diverse urban scenarios.
  • The system proved effective across different lighting conditions (morning, evening) and in three distinct urban test areas.
  • Performance evaluations showed consistent outperformance compared to existing methods, utilizing a single sensor.

Conclusions:

  • The developed visual-based system offers a viable and effective solution for mobile robot localization in GPS-denied or degraded urban environments.
  • The approach successfully leverages scene text recognition and external APIs for accurate geospatial positioning.
  • This research highlights the potential of vision-based techniques to overcome the limitations of traditional localization methods in complex real-world applications.