A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle
This addresses localization challenges for UAVs in automation tasks, but appears incremental as it builds on existing filtering methods with a specific implementation.
The paper tackled the problem of accurate localization for unmanned aerial vehicles by proposing a minimum energy filter for velocity-aided pose estimation on the extended special Euclidean group, demonstrating its performance through simulation.
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.