Event-based Star Tracking via Multiresolution Progressive Hough Transforms

TL;DR

This paper introduces an efficient event-based star tracking algorithm using multiresolution Hough Transforms and rotation averaging, enabling fast, accurate attitude estimation on resource-limited hardware with asynchronous data processing.

Contribution

The paper presents a novel event-based star tracking method that leverages multiresolution Hough Transforms and rotation averaging for improved efficiency and accuracy over existing schemes.

Findings

More efficient than state-of-the-art event-based motion estimation

Provides accurate relative rotations from event streams

Feasible for asynchronous operation on standard hardware

Abstract

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.