  Collaborative Localisation has been studied extensively in recent years as a
way to improve pose estimation of unmanned aerial vehicles in challenging
environments. However little attention has been paid toward advancing the
underlying filter design beyond standard Extended Kalman Filter-based
approaches. In this paper, we detail a discrete-time collaborative localisation
filter using the deterministic minimum-energy framework. The filter
incorporates measurements from an inertial measurement unit and models the
effects of sensor bias and gravitational acceleration. We present a simulation
based on real-world vehicle trajectories and IMU data that demonstrates how
collaborative localisation can improve performance over single-vehicle methods.
