Inertial Collaborative Localisation for Autonomous Vehicles using a Minimum Energy Filter
This work addresses localization accuracy for autonomous vehicles, but it is incremental as it builds on existing collaborative localization with a new filter design.
The paper tackled the problem of improving pose estimation for autonomous vehicles in challenging environments by developing a discrete-time collaborative localization filter using the deterministic minimum-energy framework, which incorporates inertial measurements and models sensor biases and gravity, and demonstrated through simulation with real-world data that it outperforms single-vehicle methods.
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.