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Obstacle detection for lake-deployed autonomous surface vehicles using RGB imagery.

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This study presents a low-cost obstacle detection system for autonomous surface vehicles (ASVs) using a single-board computer and camera. The system effectively identifies obstacles in lakes despite challenging conditions like shorelines and glint.

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Area of Science:

  • Robotics
  • Computer Vision
  • Environmental Monitoring

Background:

  • Autonomous surface vehicles (ASVs) require robust obstacle detection for safe lake operations.
  • Variable lake environments present challenges such as shorelines, land-sky boundaries, and glint.
  • Existing systems may be too costly or complex for small, lake-deployed ASVs.

Purpose of the Study:

  • To develop and test an effective, low-cost obstacle detection system for small ASVs operating in lakes.
  • To address the unique challenges of lake environments, including shoreline presence and variable lighting.
  • To demonstrate the feasibility of using single-board computers for real-time ASV obstacle detection.

Main Methods:

  • An innovative gradient-based image processing algorithm was developed for low-angle camera views.
  • Correlation-based multi-frame analysis was employed to differentiate true and false positive obstacle detections.
  • The system was implemented on a Raspberry Pi 3 Model B computer using a consumer-grade camera.

Main Results:

  • The obstacle detection algorithm processed images at approximately 3-4 Hz under operational conditions.
  • Extensive testing on Lake Geneva confirmed the system's effectiveness in variable lake conditions.
  • The system successfully demonstrated obstacle detection despite shoreline interference and glint.

Conclusions:

  • Single-board computers are suitable for developing effective and low-cost obstacle detection systems for ASVs.
  • The developed algorithm provides a viable solution for autonomous navigation in complex lake environments.
  • This technology enhances the safety and operational capabilities of small ASVs.