  Star trackers are state-of-the-art attitude estimation devices which function
by recognising and tracking star patterns. Most commercial star trackers use
conventional optical sensors. A recent alternative is to use event sensors,
which could enable more energy efficient and faster star trackers. However,
this demands new algorithms that can efficiently cope with high-speed
asynchronous data, and are feasible on resource-constrained computing
platforms. To this end, we propose an event-based processing approach for star
tracking. Our technique operates on the event stream from a star field, by
using multiresolution Hough Transforms to time-progressively integrate event
data and produce accurate relative rotations. Optimisation via rotation
averaging is then used to fuse the relative rotations and jointly refine the
absolute orientations. Our technique is designed to be feasible for
asynchronous operation on standard hardware. Moreover, compared to
state-of-the-art event-based motion estimation schemes, our technique is much
more efficient and accurate.
