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A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation.

Francesco de Gioia1, Gabriele Meoni2, Gianluca Giuffrida1

  • 1Department of Information Engineering, University of Pisa Via Girolamo Caruso 16, 56122 Pisa, PI, Italy.

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Summary

Future spacecraft will navigate autonomously using image-based optical navigation. This study introduces a robust, computationally efficient pipeline for estimating celestial body parameters from onboard camera images, even with noise and distortions.

Keywords:
RANSACcircle fittingimage processingoptical navigationradius estimationvisual based navigation

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Area of Science:

  • Spacecraft Navigation
  • Autonomous Systems
  • Image Processing

Background:

  • Increasing numbers of spacecraft necessitate autonomous navigation to reduce operator burden.
  • Image-based optical navigation offers a cost-effective solution for autonomous spacecraft control.
  • Current methods face challenges with resource-constrained environments and noisy data.

Discussion:

  • A novel image processing pipeline is proposed for estimating celestial body centers and radii from onboard images.
  • The pipeline is optimized for resource-constrained applications, featuring a simple processing flow and minimal memory footprint.
  • The RANSAC algorithm is employed for robust parameter estimation, handling Gaussian noise, distortions, and frame drops.

Key Insights:

  • The proposed pipeline accurately estimates planetary and lunar parameters (center, radius) from onboard imagery.
  • The system demonstrates resilience against common image corruptions like noise and distortions.
  • Computational efficiency and low memory usage make it suitable for embedded spacecraft systems.

Outlook:

  • Further validation with real-world mission data is recommended.
  • Integration into future autonomous spacecraft navigation systems is anticipated.
  • Potential for adaptation to other celestial body detection and parameter estimation tasks.