Inertial Collaborative Localisation for Autonomous Vehicles using a Minimum Energy Filter

TL;DR

This paper introduces a novel discrete-time collaborative localisation filter for autonomous vehicles that uses a minimum-energy framework, integrating IMU data and modeling sensor biases to enhance pose estimation in challenging environments.

Contribution

It advances filter design beyond standard EKF approaches by applying a deterministic minimum-energy filter for collaborative localisation with inertial measurements.

Findings

Simulation shows improved localisation accuracy over single-vehicle methods.

Incorporates sensor bias and gravity effects into the filter model.

Demonstrates effectiveness with real-world vehicle trajectories and IMU data.

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.