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Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal.

Charalambos Theodorou1,2, Vladan Velisavljevic1, Vladimir Dyo1

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Summary
This summary is machine-generated.

This study introduces a Visual Simultaneous Localization and Mapping (vSLAM) system using ORB-SLAM3 and YOLOR object detection to improve accuracy in dynamic indoor environments. The enhanced system effectively handles moving objects, achieving higher precision than existing methods.

Keywords:
object detectionsimultaneous localization and mapping (SLAM)visual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual Simultaneous Localization and Mapping (vSLAM) systems are crucial for robot navigation and augmented reality.
  • Dynamic objects in indoor environments pose significant challenges to vSLAM accuracy and stability.
  • Existing vSLAM methods often struggle with the presence of moving objects, leading to errors in localization and mapping.

Purpose of the Study:

  • To develop and evaluate an improved vSLAM system capable of robust performance in dynamic indoor environments.
  • To enhance the accuracy and stability of visual odometry and position estimation in the presence of moving objects.
  • To integrate advanced object detection techniques into a state-of-the-art vSLAM framework.

Main Methods:

  • A novel vSLAM system was proposed, integrating the ORB-SLAM3 framework with the YOLOR object detection model.
  • YOLOX object detection was applied to extracted feature points to identify and manage dynamic elements.
  • A tracking thread was utilized to eliminate the influence of dynamic moving objects on static feature points used for camera positioning.

Main Results:

  • The proposed vSLAM system achieved 89.54% accuracy in dynamic indoor environments.
  • The system demonstrated a 2-4% improvement in accuracy compared to established methods like VPS-SLAM and DS-SLAM.
  • Validation was performed on a custom dataset featuring diverse indoor/outdoor train station scenes with high densities of people and dynamic objects.

Conclusions:

  • The integration of YOLOR object detection with ORB-SLAM3 significantly enhances vSLAM performance in dynamic indoor settings.
  • The method effectively mitigates the negative impact of moving objects on localization and mapping accuracy.
  • This approach offers a more reliable solution for vSLAM applications in complex, real-world environments.