Experimental photogrammetry of lunar images

Wu, S. S. C.; Moore, H. J.

doi:10.3133/pp1046d

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Therefore, as an example, consider a spacecraft placed randomly (with a uniform distribution) around the lunar sphere, thus creating a situation where we randomly image with equal probability any part of the lunar globe. We assume a camera with a full field-of-view (FOV) of about 73.7 × 73.7 deg (same as the Apollo metric camera [38,143]) and with a 2, 200 × 2, 200 pixel focal plane array (creating a 4.8 MPixel image).…”

Section: Numerical Resultsmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

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It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera’s location. This so-called “lost-in-space” crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

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Section: Numerical Resultsmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

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“…In the physical camera system, the detector lies inside the camera housing on (or near) the focal plane of the optical system. Older camera systems (e.g., the Apollo mapping/metric camera [25]) used film to record images, whereas contemporary systems create a digital image with a 2D array of photodetectors (e.g., CCD and CMOS imaging sensors [26]). We generically refer to these detectors as focal plane arrays (FPAs).…”

Section: Geometric Preliminaries a Model For A Projective Cameramentioning

confidence: 99%

A Tutorial on Horizon-Based Optical Navigation and Attitude Determination With Space Imaging Systems

Christian

2021

IEEE Access

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Images of a nearby celestial body collected by a camera on an exploration spacecraft contain a wealth of actionable information. This work considers how the apparent location of the observed body's horizon in a digital image may be used to infer the relative position, attitude, or both. When the celestial body is a sphere, spheroid, or ellipsoid (as is the case for most large bodies in the Solar System), the projected horizon in an image is a conic-usually an ellipse at large distances and a hyperbola at small distances. This work develops non-iterative and analytically exact methods for every case (all combinations of unknown state parameters and quadric shapes), completely superseding older horizon-based methods that are iterative, approximate, or both. Some of the analytic methods presented in this work are new. Recognizing that these developments build on techniques that may be unfamiliar to many spacecraft navigators, this work is fashioned as a tutorial. Descriptive illustrations and numerical examples are provided to make concepts clear and to validate the proposed algorithms.

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“…Therefore, as an example, consider a spacecraft placed randomly (with a uniform distribution) around the lunar sphere, thus creating a situation where we randomly image with equal probability any part of the lunar globe. We assume a camera with a full field-of-view (FOV) of about 73.7 × 73.7 deg (same as the Apollo metric camera [36,138]) and with a 2, 200 × 2, 200 pixel focal plane array (creating a 4.8 MPixel image).…”

Section: Sensitivity To Crater Rim Fit and Viewing Geometrymentioning

confidence: 99%

Lunar Crater Identification in Digital Images

Christian,

Derksen,

Watkins

2020

Preprint

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It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

show abstract

Experimental photogrammetry of lunar images

Cited by 8 publications

References 6 publications

Lunar Crater Identification in Digital Images

Lunar Crater Identification in Digital Images

A Tutorial on Horizon-Based Optical Navigation and Attitude Determination With Space Imaging Systems

Lunar Crater Identification in Digital Images

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