Spacecraft optical navigation (OpNav) systems process a sequence of images with celestial bodies against a star field background to estimate the position of the vehicle. While attitude is sometimes available from an onboard star tracker, it is often desirable to recognize the background stars in OpNav images to better align the image. While many image processing algorithms exist for finding stars, efficiency and reliability remain key issues in the presence of extended bodies (e.g. the Moon, Earth), especially when attempting to solve the full lost-in-space problem. Star outliers (stars identified with high residuals) can appear in the camera field of view, and using them in attitude estimation or camera calibration will lead to less accurate results. Therefore, new and robust approaches are required to remove these outliers before any further processing.
The emphasis of this work is on developing a simple and robust iterative technique to detect and reject the outliers that could be found in any frame during the lost-in-space attitude determination or during camera calibration. These outliers are determined based on the residuals of the centroids of the detected stars, and their corresponding location using a star catalog. If the residuals exceed a predetermined threshold value, the object will be identified as an outlier and will be removed before another attitude determination and calibration iteration is performed.
The performance for both attitude determination and on-orbit camera calibration are improved by an almost two-fold increase in accuracy when applying this outlier rejection technique.
OPTICAL NAVIGATION ATTITUDE ESTIMATION AND CALIBRATION PERFORMANCE IMPROVEMENT USING OUTLIER REJECTION
Proceedings of the 44th Annual American Astronautical Society Guidance, Navigation, and Control Conference, 2022 ; Kapitel : 56 ; 993-1006
2024-01-01
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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