Abstract
High precision tracking of stars both from ground and from orbit is a vital capability that enables autonomous alignment of both satellite and ground-based telescopes. Event cameras provide high-dynamic range, high temporal resolution, low latency asynchronous “event” data that captures illumination changes in a scene. Such data is ideally suited for estimating star motion since it has minimal image blur and can capture low-intensity changes in irradiation typical of astronomical observations. In this work, we propose a novel Asynchronous Event-based Star Tracker that processes each event asynchronously to update a Kalman filter that estimates star position and velocity in an image. The proposed tracking method is validated on real and simulated data and shows state-of-that-art tracking performance against existing approach.
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Acknowledgements
We would like to thank reviewers for the excellent feedback. We would also like to thank SmartSat CRC for funding the research, and Professor John Richards for his generous assistance in collecting the real event dataset using his telescope.
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Ng, Y., Latif, Y., Chin, TJ., Mahony, R. (2023). Asynchronous Kalman Filter for Event-Based Star Tracking. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13801. Springer, Cham. https://doi.org/10.1007/978-3-031-25056-9_5
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