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  1. Home
  3. Ydd-slam: Indoor Dynamic Visual Slam Fusing Yolov5 With Depth Information.
  1. Home
  3. Ydd-slam: Indoor Dynamic Visual Slam Fusing Yolov5 With Depth Information.

Related Experiment Video

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information.

Peichao Cong1, Junjie Liu1, Jiaxing Li1

  • 1School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China.

Sensors (Basel, Switzerland)
|December 9, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces YDD-SLAM, a visual simultaneous localization and mapping (VSLAM) algorithm improving robot navigation in dynamic environments. It enhances positioning accuracy by effectively identifying and removing dynamic objects.

Keywords:
ORB-SLAM3YOLOv5depth information fusiondynamic VSLAMobject classification

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual Simultaneous Localization and Mapping (VSLAM) is crucial for robot navigation.
  • Existing VSLAM algorithms struggle with accuracy and real-time performance in dynamic environments, often failing when dynamic objects are prevalent.

Purpose of the Study:

  • To propose YDD-SLAM, an enhanced indoor dynamic VSLAM algorithm.
  • To improve positioning accuracy and robustness in dynamic environments for VSLAM.

Main Methods:

  • YDD-SLAM integrates YOLOv5 object detection with ORB-SLAM3.
  • Objects are categorized by motion and depth; dynamic features are identified and eliminated using depth information.
  • Multiple feature point optimization strategies are employed for dynamic environments.

Main Results:

  • YDD-SLAM demonstrated significantly improved accuracy compared to ORB-SLAM3 in tests.
  • The algorithm effectively handles environments with a high proportion of dynamic objects.
  • Robust performance was validated on a public dataset and in a real-world dynamic scenario.

Conclusions:

  • YDD-SLAM offers a robust solution for VSLAM in dynamic indoor environments.
  • The proposed method enhances navigation accuracy and reliability for autonomous robots.
  • This research lays the groundwork for practical dynamic VSLAM applications.