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Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface
Binghua Shi1, Jia Guo2, Chen Wang3
1Hubei University Of Economics, Information and Communication Engineering, Wuhan, 430000, China.
Scientific Reports
|June 21, 2022
Summary
This study introduces a visual method to assess navigation scene complexity for autonomous surface vehicles (USVs). The approach improves testing efficiency by accurately modeling complexity using image textures and a novel clustering technique.
Area of Science:
- Robotics and Autonomous Systems
- Computer Vision
- Artificial Intelligence
Background:
- Autonomous navigation systems for Unmanned Surface Vehicles (USVs) face challenges with testing efficiency and the long-tail problem.
- Accurate perception of navigation scene complexity is crucial for robust autonomous operation.
Purpose of the Study:
- To develop a visual image-based method for perceiving navigation scene complexity in USVs.
- To establish a mathematical model linking visual features to navigation scene complexity.
- To enhance the testing efficiency of autonomous navigation systems.
Main Methods:
- Summarizing typical complex elements and categorizing navigation scenes into four complexity levels.
- Extracting textural features using Gray Level Co-occurrence Matrix (GLCM) and Tamura coarseness to create feature vectors.
- Proposing a novel Paired Bare Bone Particle Swarm Clustering (PBBPSC) method for complexity classification and calculating exact complexity values via interval mapping.
Main Results:
- The proposed method effectively classifies navigation scene complexity levels.
- The method accurately calculates the exact value of navigation scene complexity.
- Experimental results demonstrate superior performance compared to existing methods on diverse datasets.
Conclusions:
- The developed visual complexity perception method significantly improves the description and accuracy of navigation scene complexity for USVs.
- This approach offers a viable solution to enhance the testing efficiency and robustness of autonomous navigation systems.
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