Geometric Calibration of the Orion Optical Navigation Camera using Star Field Images

Christian, John A.; Benhacine, Lylia; Hikes, Jacob; D’Souza, Christopher

doi:10.1007/s40295-016-0091-3

Cited by 25 publications

(28 citation statements)

References 11 publications

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“…In-orbit geometric calibration and validation of high-resolution Earth-observation satellites is important because various impulses and vibrations during the launch process may have affected the satellite’s payload [1,2,3,4,5,6,7,8]. Therefore, the in-orbit geometric calibration process determines the focal length, the distortions of lenses, CCD (charge-coupled device) alignments, and other geometric distortions.…”

Section: Introductionmentioning

confidence: 99%

Geometric Calibration and Validation of Kompsat-3A AEISS-A Camera

Seo

Lee

et al. 2016

Sensors

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Kompsat-3A, which was launched on 25 March 2015, is a sister spacecraft of the Kompsat-3 developed by the Korea Aerospace Research Institute (KARI). Kompsat-3A’s AEISS-A (Advanced Electronic Image Scanning System-A) camera is similar to Kompsat-3’s AEISS but it was designed to provide PAN (Panchromatic) resolution of 0.55 m, MS (multispectral) resolution of 2.20 m, and TIR (thermal infrared) at 5.5 m resolution. In this paper we present the geometric calibration and validation work of Kompsat-3A that was completed last year. A set of images over the test sites was taken for two months and was utilized for the work. The workflow includes the boresight calibration, CCDs (charge-coupled devices) alignment and focal length determination, the merge of two CCD lines, and the band-to-band registration. Then, the positional accuracies without any GCPs (ground control points) were validated for hundreds of test sites across the world using various image acquisition modes. In addition, we checked the planimetric accuracy by bundle adjustments with GCPs.

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Section: Introductionmentioning

confidence: 99%

Geometric Calibration and Validation of Kompsat-3A AEISS-A Camera

Seo

Lee

et al. 2016

Sensors

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“…Robotic Technology Center (WVRTC). With research being conducted on camerabased and LIDAR-based optical navigation as well as attitude determination, the ASEL has helped make WVU an ever growing competitor in space navigation research [2,[4][5][6][7][8][9][10][11][12]. One problem of particular interest in the area of spacecraft navigation is known as the "lost-in-space" problem.…”

Section: 6mentioning

confidence: 99%

“…After the centroid pixel coordinate is determined from the centroiding algorithm, a calibration process is used to determine the sensor frame line-of-sight directions to each star. One particular method of this process has been proposed by [11].…”

Section: Thesis Organizationmentioning

confidence: 99%

“…This is done through a star centroiding process, for which there are a variety of very well-known techniques [15][16][17]. The centroid pixel coordinates may be related to line-of-sight directions in the sensor frame through a straightforward calibration process [11]. Once an observed quad is matched to the index, the correspondence between the observed stars in the quad and the stars in the star catalog is known (this correspondence is known because you know which catalog stars were used to create the best-match hash code in the index).…”

Section: Star Measurements In the Sensor Framementioning

confidence: 99%

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A geometric hashing technique for star pattern recognition

Gerhard¹

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With the present interest in deep space missions by NASA and other space agencies, various techniques are continuously being developed and tested for accurate spacecraft attitude determination. One particularly important problem is the situation where the spacecraft has no a priori attitude information-the so called "lost-in-space" problem. This thesis presents a novel method for recognizing star patterns in an image with no a priori attitude information, with applications to spacecraft star trackers and other similar attitude sensors. Specifically, a geometric hashing approach is proposed that describes star patterns by the four interior star angles that form the perimeter of a star quad. A technique is presented for labeling these four interior star angles to make the hash code both unique as well as invariant to sensor attitude or star observation order. Using this approach, an index of star quad hash codes is created from a star catalog and the result is stored in a k-d tree. When an image of a star field is obtained, observed star quads are created and the index is searched for the best match using a nearest neighbor algorithm. A verification process is used to improve robustness. This thesis gives an extensive overview of the process that was created to recognize and disregard possible false positives in the star identification algorithm, while still producing accurate results and correctly identifying stars at a high success rate. Performance of the new star identification approach is demonstrated through a Monte Carlo analysis that considered 100,000 random attitudes. It is found that the proposed approach successfully identifies star patterns in 99.79% of the cases and decides that no match can be reliably made in 0.21% of the cases. Incorrect star match were observed in only 0.001% of the test cases. The effect of these very infrequent misidentifications on the resulting attitude estimate were found to be benign.

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“…The geometrical model for the projection of a point (X, Y, Z) in 3D space onto a 2D image point (x, y) is perfect perspective projection. This can be described by the pinhole camera model, shown in Figure 2.2 [1,3,8,30].…”

Section: Perfect Perspective Projectionmentioning

confidence: 99%

Optical Aberrations and their Effect on the Centroid Location of Unresolved Objects

Benhacine¹

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Optical Aberrations and their Effect on the Centroid Location of Unresolved Objects Lylia Benhacine Optical data from point sources of light plays a key role in systems from many different industries. In real optical systems, the photons from point sources are distributed in a pattern on the focal plane. This pattern is described by the Point Spread Function (PSF). Centroiding, defined here as calculating the center of a PSF, provides data that can subsequently be used in various state estimation problems. Thus, it is crucial to determine expected centroid accuracy so that uncertainty can be accounted for. Manufacturing defects and real-world effects affect the structure of a PSF. Much of this structure is often well-captured by the five classic optical aberrations. This thesis prevents a novel framework for simulating PSFs affected by optical aberrations, then uses this simulation to quantify the accuracy of various centroiding algorithms in the presence of optical aberrations. The method of PSF simulation leverages Johnson distributions, which is a method of simulating distributions as a function of the desired mean, variance, skewness, and kurtosis. This method is chosen because of the flexibility and ease-of-visualization it provides the analyst. In addition, the analytic true centers of the Johnson distributions have been derived for use in analysis as the true centroid. The three centroiding algorithms analyzed are the Center of Intensity (COI) algorithm, the Cross-Correlation (CC) algorithm, and the Model Matching (MM) algorithm. It is found that in the case of symmetric, Gaussian PSFs, all three algorithms find the true centroid exactly. However, performance deteriorates for an asymmetric PSF, especially for the CC and MM algorithm. This is because both of these algorithms assume a 2D Gaussian structure. When that assumption no longer holds, the algorithms do not perform as well. This work concludes that PSFs highly affected by asymmetric optical aberrations should expect lower centroiding accuracy when using the CC and MM algorithms. I would like to thank my advisor, Dr. John Christian. Thank you for sharing your guidance and knowledge the past two years. I've become a better engineer because of my experience in your lab, and I'm very appreciative for the opportunity. Thank you to the members of ASEL for a great research and lab experience. I can't wait to see all the great things you all will do. Last but not least, thank you to my parents, Mohamed and Lalia, my sister, Lyna, and Ali, who have supported me so much during this degree. I wouldn't be here without it, and I love you all very much.

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Geometric Calibration of the Orion Optical Navigation Camera using Star Field Images

Cited by 25 publications

References 11 publications

Geometric Calibration and Validation of Kompsat-3A AEISS-A Camera

Geometric Calibration and Validation of Kompsat-3A AEISS-A Camera

A geometric hashing technique for star pattern recognition

Optical Aberrations and their Effect on the Centroid Location of Unresolved Objects

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