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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
Summary
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.
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.
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