Abstract
Despite the efforts for precise alignment of imaging sensors and attitude sensors before launch, the accuracy of pre-launch alignment is limited. The misalignment between attitude frame and camera frame is especially important as it is related to the localization error of the spacecraft, which is one of the essential factors of satellite image quality. In this paper, a framework for camera misalignment estimation is presented with its application to a high-resolution earth-observation satellite—Deimos-2. The framework intends to provide a solution for estimation and correction of the camera misalignment of a spacecraft, covering image acquisition planning to mathematical solution of camera misalignment. Considerations for effective image acquisition planning to obtain reliable results are discussed, followed by a detailed description on a practical method for extracting many GCPs automatically using reference ortho-photos. Patterns of localization errors that commonly occur due to the camera misalignment are also investigated. A mathematical model for camera misalignment estimation is described comprehensively. The results of simulation experiments showing the validity and accuracy of the misalignment estimation model are provided. The proposed framework was applied to Deimos-2. The real-world data and results from Deimos-2 are presented.
Highlights
The alignments of sensors and actuators inside a spacecraft are measured carefully in laboratory before launch
The residual RMSE is about 1/5 of the original RMSE, showing that the localization errors could be well corrected by the estimated camera misalignment
This paper presented a framework for camera misalignment estimation and supporting results
Summary
The alignments of sensors and actuators inside a spacecraft are measured carefully in laboratory before launch. A common approach for on-orbit boresight alignment calibration is based on the usage of ground control points (GCPs) and a physical sensor model. Klančar et al suggested an image-based attitude control mechanism that correlates the spacecraft camera image and the reference image on the fly [22] This approach eliminates the need of measuring the boresight alignment, it is impractical due to the limited on-board resource to have a high-resolution GCP database on-board and perform the real-time feature matching. A framework for on-orbit camera misalignment estimation of earth-observation satellites is presented It provides an all-in-one solution from the planning of ground-target image acquisition to the estimation of the camera misalignment. The steps of camera misalignment estimation such as image planning, automated GCP extraction, localization error pattern analysis, and the mathematical model for camera misalignment estimation are explained.
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