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PLY-SLAM: Semantic Visual SLAM Integrating Point-Line Features with YOLOv8-seg in Dynamic Scenes.

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Approach to Semantic Visual SLAM for Bionic Robots Based on Loop Closure Detection with Combinatorial Graph Entropy

Dazheng Wang1, Jingwen Luo1,2

  • 1School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.

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Summary
This summary is machine-generated.

This study introduces a semantic visual SLAM (vSLAM) algorithm for bionic robots, enhancing loop closure detection using combinatorial graph entropy. The method significantly improves trajectory accuracy and reduces keyframes in dynamic environments.

Keywords:
bionic robotscombinatorial graph entropycomplex dynamic scenesloop closure detectionvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • SLAM systems on bionic robots struggle with dynamic environments.
  • Dynamic objects and environmental changes disrupt SLAM performance.
  • Existing methods lack robustness in complex, changing scenarios.

Purpose of the Study:

  • To develop a robust semantic visual SLAM (vSLAM) algorithm for bionic robots.
  • To improve loop closure detection accuracy in dynamic environments.
  • To enhance overall SLAM performance by addressing dynamic object interference.

Main Methods:

  • Utilizing YOLOv8-seg for dynamic feature detection and Mean Absolute Deviation (MAD) for edge point refinement.
  • Implementing a keyframe selection strategy combining semantic information, object coordinates, and feature point variation.
  • Developing a loop closure detection method based on combinatorial graph entropy using weighted and unweighted keyframe graphs.

Main Results:

  • The proposed loop closure detection shows superior precision and recall over the bag-of-words (BoW) model in real-world scenes.
  • Achieved an average 97.01% improvement in absolute trajectory accuracy in high-dynamic sequences compared to ORB-SLAM2.
  • Reduced the number of extracted keyframes by an average of 61.20%.

Conclusions:

  • The semantic vSLAM algorithm with combinatorial graph entropy effectively handles dynamic environments for bionic robots.
  • The approach significantly enhances localization accuracy and efficiency.
  • This method offers a robust solution for SLAM in complex, real-world scenarios.