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Improving ORB-SLAM3 Accuracy in Dynamic Scenes with YOLO11 Segmentation.

Renata Raffaine Villegas1, Anselmo Rafael Cukla2, Gabriel Alejandro Tarnowski3

  • 1Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas (UNICAMP), Rua Mendenleyv, 200, Cidade Universitária, Campinas, São Paulo 13083-860, Brazil.

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Summary
This summary is machine-generated.

This study enhances ORB-SLAM3 with YOLOv11 instance segmentation for dynamic environments. The improved system significantly reduces visual SLAM errors in real-world robotics and datasets.

Keywords:
ORB-SLAM3ROS2YOLOvisual SLAM

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Area of Science:

  • Robotics and Computer Vision
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Traditional Visual SLAM systems struggle with accuracy in dynamic environments.
  • ORB-SLAM3, a popular SLAM system, is susceptible to performance degradation due to moving objects.

Purpose of the Study:

  • To enhance ORB-SLAM3 for robust performance in dynamic environments.
  • To integrate instance segmentation for dynamic feature exclusion in SLAM.

Main Methods:

  • Developed YOLOv11-ORB-SLAM3 by integrating a YOLOv11 instance segmentation module into ORB-SLAM3.
  • Tested the system with stereo and RGB-D cameras on the TUM RGB-D dataset and real-world mobile robot experiments.

Main Results:

  • YOLOv11-ORB-SLAM3 significantly outperforms the original ORB-SLAM3 in dynamic scenarios.
  • Achieved a 93% error reduction on the TUM RGB-D dataset.
  • Maintained computational efficiency.

Conclusions:

  • The proposed YOLOv11-ORB-SLAM3 system offers a robust and efficient solution for visual SLAM in dynamic environments.
  • Demonstrated viability for real-world robotic applications requiring accurate localization amidst moving objects.