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

Inertial Collaborative Localisation for Autonomous Vehicles using a Minimum Energy Filter