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Related Experiment Video

Updated: Sep 18, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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PLY-SLAM: Semantic Visual SLAM Integrating Point-Line Features with YOLOv8-seg in Dynamic Scenes.

Huan Mao1, Jingwen Luo1,2

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

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a semantic visual simultaneous localization and mapping (vSLAM) system using point-line features and YOLOv8-seg. It enhances robustness and accuracy in challenging environments by effectively handling dynamic objects.

Keywords:
YOLOv8-segdynamic sceneloop-closure detectionpoint-line featuressemantic visual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional point-feature-based visual SLAM (vSLAM) struggles with robustness and accuracy in dynamic and low-texture environments.
  • Existing methods often fail to adequately address the challenges posed by dynamic objects and sparse environmental features.

Purpose of the Study:

  • To develop a robust and accurate semantic vSLAM system for dynamic and low-texture environments.
  • To improve localization accuracy by fusing point-line features with semantic information from YOLOv8-seg.

Main Methods:

  • Implemented a high-performance 3D line-segment extraction and fitting method using sampled points and RANSAC.
  • Utilized Delaunay triangulation for geometric mapping and dynamic feature point detection by analyzing topological changes.
  • Integrated YOLOv8-seg instance labels to accurately remove dynamic feature points and employed a loop-closure mechanism fusing point-line features with instance-level matching.

Main Results:

  • The proposed semantic vSLAM approach demonstrated superior performance in simulations and experiments.
  • Effective fusion of point-line features and semantic segmentation improved robustness and localization accuracy.
  • Accurate removal of dynamic feature points and reliable loop-closure detection were achieved.

Conclusions:

  • The semantic vSLAM method significantly enhances performance in challenging environments compared to traditional approaches.
  • Fusing geometric features (point-line) with semantic understanding (YOLOv8-seg) is crucial for robust vSLAM.
  • The developed system offers a promising solution for accurate and reliable navigation in complex real-world scenarios.