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YPL-SLAM: A Simultaneous Localization and Mapping Algorithm for Point-line Fusion in Dynamic Environments.

Xinwu Du1,2,3, Chenglin Zhang1, Kaihang Gao1

  • 1College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China.

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|July 27, 2024
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Summary

This study introduces YPL-SLAM, a novel Simultaneous Localization and Mapping (SLAM) algorithm designed for robust mobile robot navigation in dynamic environments. YPL-SLAM significantly improves accuracy and operational speed by effectively handling dynamic objects.

Keywords:
YOLOv5sYPL-SLAMdynamic environmentpoint–line fusionvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for mobile robot autonomous navigation.
  • Existing visual SLAM algorithms struggle with accuracy and tracking in dynamic environments due to moving objects.
  • Filtering dynamic features can lead to reduced performance in complex scenarios.

Purpose of the Study:

  • To develop a robust SLAM algorithm, YPL-SLAM, capable of accurate navigation in environments with dynamic objects.
  • To enhance the accuracy and operational speed of visual SLAM systems.
  • To address the limitations of traditional SLAM methods in handling environmental dynamism.

Main Methods:

  • YPL-SLAM builds upon ORB-SLAM2, integrating target recognition and region segmentation to classify dynamic, potential dynamic, and static areas.
  • The RANSAC method with polar geometric constraints is employed to determine the state of potential dynamic regions.
  • Dynamic feature points are removed, line features are extracted from static regions, and point-line fusion optimization is performed.

Main Results:

  • YPL-SLAM demonstrated superior accuracy compared to ORB-SLAM2, with a maximum improvement of 96.1% on the TUM dataset.
  • The algorithm showed significantly enhanced operating speed compared to Dyna-SLAM.
  • Experiments confirmed the robustness and accuracy of YPL-SLAM in handling dynamic environments.

Conclusions:

  • YPL-SLAM effectively addresses the challenges posed by dynamic objects in visual SLAM.
  • The proposed point-line fusion strategy enhances system robustness and accuracy.
  • YPL-SLAM represents a significant advancement for autonomous robot navigation in real-world, dynamic settings.