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A Real-Time Semantic Map Production System for Indoor Robot Navigation.

Raghad Alqobali1, Reem Alnasser2, Asrar Rashidi2

  • 1Saudi Data and AI Authority, Riyadh 12382, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a semantic map production system for mobile robots, enhancing indoor navigation. The system uses LiDAR and vision, achieving 78.86% map construction accuracy for advanced autonomous tasks.

Keywords:
path planningrobot navigationrobot visionsemantic maps

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional grid maps lack semantic information crucial for advanced robot tasks.
  • Enhancing indoor navigation requires richer environmental understanding beyond geometric data.

Purpose of the Study:

  • To develop a semantic map production system for mobile robot indoor navigation.
  • To integrate LiDAR and vision-based systems for rich semantic map generation.
  • To validate the system's efficiency and accessibility using low-cost hardware and ROS.

Main Methods:

  • Employed LiDAR technology and a vision-based system for data acquisition.
  • Utilized the You Only Look Once (YOLO) v3 object detection model for semantic information extraction.
  • Validated the system in simulation experiments within indoor environments using the Robot Operating System (ROS).

Main Results:

  • Achieved efficient object recognition accuracy and object localization.
  • Demonstrated high precision in semantic map production.
  • Attained an average map construction accuracy of 78.86%.

Conclusions:

  • The proposed system effectively produces semantic maps for enhanced indoor robot navigation.
  • The integration of low-cost hardware and YOLO v3 makes the system accessible and efficient.
  • The system facilitates advanced autonomous tasks by providing rich environmental context.