A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle

TL;DR

This paper introduces a minimum energy filter leveraging Lie-group symmetry for accurate UAV localization by fusing IMU and landmark data, with an asynchronous implementation suitable for real-world applications.

Contribution

It presents a novel minimum energy filter on the extended special Euclidean group that effectively combines IMU and landmark measurements for UAV pose estimation.

Findings

Demonstrates robust performance in simulation

Effectively fuses high and low bandwidth sensor data

Provides a practical asynchronous implementation

Abstract

Accurate localisation of unmanned aerial vehicles is vital for the next generation of automation tasks. This paper proposes a minimum energy filter for velocity-aided pose estimation on the extended special Euclidean group. The approach taken exploits the Lie-group symmetry of the problem to combine Inertial Measurement Unit (IMU) sensor output with landmark measurements into a robust and high performance state estimate. We propose an asynchronous discrete-time implementation to fuse high bandwidth IMU with low bandwidth discrete-time landmark measurements typical of real-world scenarios. The filter's performance is demonstrated by simulation.