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Lunar Crater Identification in Digital Images.

John A Christian1, Harm Derksen2, Ryan Watkins3

  • 1Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

The Journal of the Astronautical Sciences
|January 10, 2022
PubMed
Summary
This summary is machine-generated.

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This study presents a mathematically rigorous method for identifying crater patterns in lunar images without prior camera location knowledge. The approach uses invariant theory for robust crater recognition and pose estimation, advancing terrain relative navigation.

Area of Science:

  • Planetary Science
  • Computer Vision
  • Robotics

Background:

  • Crater identification in lunar images is crucial for terrain relative navigation (TRN) and image registration.
  • Previous methods relied on heuristic schemes, limiting performance in general scenarios.
  • The

Purpose of the Study:

  • To develop a mathematically rigorous solution for the general crater identification problem in lunar imagery.
  • To enable accurate crater pattern recognition without prior camera pose information.
  • To advance the capabilities of crater-based TRN systems.

Main Methods:

  • Developed a mathematically rigorous framework for analyzing elliptical crater rims under perspective projection.
  • Utilized invariant theory to create viewpoint-invariant descriptors for crater patterns.
Keywords:
ConicsCratersHEALPixInvariant theoryLunar explorationPattern recognitionProjective geometrySpacecraft navigationTerrain relative navigation

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  • Introduced novel techniques for pose computation and crater rim correspondence evaluation.

    • Main Results:

    • Established conditions for the recognizability of crater patterns in perspective images.
    • Demonstrated the effectiveness of invariant descriptors for robust crater identification.
    • Validated new pose estimation and correspondence techniques on synthetic and real lunar images.

    Conclusions:

    • The proposed invariant-theory-based approach provides a provably robust solution to the general crater identification problem.
    • This work significantly improves upon heuristic methods, enabling reliable navigation and registration in diverse imaging conditions.
    • The developed techniques offer a foundation for enhanced autonomous operations in lunar exploration.