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arXiv · 2104.05897

Inertial Collaborative Localisation for Autonomous Vehicles using a Minimum Energy Filter

Jack Henderson·Mohammad Zamani·Robert Mahony·Jochen Trumpf
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Abstract

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.

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Jack Henderson, Mohammad Zamani, Robert Mahony, Jochen Trumpf. 2021-04-13. Inertial Collaborative Localisation for Autonomous Vehicles using a Minimum Energy Filter. https://arxiv.org/abs/2104.05897

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