Non-Dimensional Star-Identification

Leake, Carl; Arnas, David; Mortari, Daniele

doi:10.3390/s20092697

Cited by 11 publications

(13 citation statements)

References 16 publications

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“…These results are consistent with the numerical experiments reported in Ref. [9], though our analysis provides a more direct explanation of why certain cases demonstrate better matching performance than others.…”

Section: ) Remarks On Poorly Calibrated Camerasupporting

confidence: 92%

“…As an example, one alternative invariant for a triad of stars is to use the three dihedral angles of the spherical triangle (e.g., as is suggested in Refs. [9] and [34]). This is illustrated in Fig.…”

Section: ) Invariants For Asterisms Of Three or More Starsmentioning

confidence: 99%

“…Good descriptor options already exist for some of these cases and this manuscript describes novel descriptors for others. Of note, there have been other attempts to construct attitude invariant descriptors for generic (wide FOV) uncalibrated cameras (e.g., [8], [9])-but, as will be shown, these are not formally invariant for the generic problem.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms may usually be posed as a solution to Wahba's Problem [14]. When the camera is poorly calibrated, there are some algorithms for narrow FOV cameras (e.g., [15]) and some for wide FOV cameras (e.g., [9])-but these both assume some knowledge (though it may be poor) of the focal length. These algorithms commonly assume square pixels and that the other three calibration terms (detector skewness and coordinates of the principal point) are known.…”

Section: Introductionmentioning

confidence: 99%

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Star Identification and Attitude Determination With Projective Cameras

Christian

2021

IEEE Access

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Images of starfields collected by a projective camera are useful for a variety of scientific and engineering purposes. This utility is exemplified by star trackers, which are amongst the most commonly used sensors for determining the attitude of modern spacecraft. While the literature on star identification and star-based attitude determination is extensive, most algorithms are developed in an ad hoc manner. This work provides a comprehensive and systematic framework for invariant-based star identification and shows most past star identification algorithms to be special cases within this framework. The new star identification framework is found to motivate new problems in attitude determination and sensor selfcalibration. Specifically, new algorithms are presented for simultaneous attitude determination and camera calibration for a generic wide field-of-view sensor using a single starfield image. In the special case where camera focal length is the only unknown calibration parameter, attitude determination performance of the new algorithm is indiscernible from a perfectly calibrated camera.

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Section: ) Remarks On Poorly Calibrated Camerasupporting

confidence: 92%

Section: ) Invariants For Asterisms Of Three or More Starsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Star Identification and Attitude Determination With Projective Cameras

Christian

2021

IEEE Access

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“…The authors have found excellent performance using either a k-d tree [11] or n-d k-vector [6], though other reasonable choices exist. We briefly remark that the 1-d k-vector [93,94] has seen [73,121] extensive use for star pattern matching in space applications [95,134], and the n-d kvector has been proposed for space applications as well [74]. The specific choice of data structure is not of primary concern in the present analysis so long as a reasonable selection is made.…”

Section: Building a Global Crater Indexmentioning

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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Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Yang²,

Xiao

et al. 2022

J. Navigation

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The fish-eye star sensor with a field of view (FOV) of 180° is an important piece of equipment for attitude determination, which improves the visibility of stars significantly. However, it also brings the star identification (star-ID) difficulties because of imprecise calibrations. Thus, a fish-eye star-ID algorithm supported by the integration of the precise point positioning/inertial navigation system (PPP/INS) is proposed. At first, a reference star map is generated in combination with the distortion model of the fish-eye camera based on the position and attitude information from the PPP/INS. Then the star points are extracted in a specific neighbourhood of the reference star points. Subsequently, the extracted star points are individually tested and identified according to angular distance error. Finally, the real-time precise attitude is determined based on the star-ID results. Experimental results show that, 270–310 stars can be identified in a fish-eye star map with an average time of 0.03 s if the initial attitude error is smaller than 1.5° and an attitude determination accuracy better than 10″ can be achieved by support from PPP/INS.

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Non-Dimensional Star-Identification

Cited by 11 publications

References 16 publications

Star Identification and Attitude Determination With Projective Cameras

Star Identification and Attitude Determination With Projective Cameras

Lunar Crater Identification in Digital Images

Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

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