Robust Localization of Aerial Vehicles via Active Control of Identical Ground Vehicles

TL;DR

This paper presents a novel active control method for positioning ground vehicles to enable UAVs to achieve centimeter-level localization accuracy in GPS-denied environments, outperforming random strategies and tolerating measurement noise.

Contribution

It introduces a game-theoretic approach for active ground vehicle positioning to facilitate UAV localization using unlabelled measurements, with real-world validation.

Findings

Achieves centimeter-level localization accuracy in experiments.

Reduces position and angular error by up to 90% compared to random positioning.

Demonstrates robustness to measurement noise.

Abstract

This paper addresses the problem of active collaborative localization in heterogeneous robot teams with unknown data association. It involves positioning a small number of identical unmanned ground vehicles (UGVs) at desired positions so that an unmanned aerial vehicle (UAV) can, through unlabelled measurements of UGVs, uniquely determine its global pose. We model the problem as a sequential two player game, in which the first player positions the UGVs and the second identifies the two distinct hypothetical poses of the UAV at which the sets of measurements to the UGVs differ by as little as possible. We solve the underlying problem from the vantage point of the first player for a subclass of measurement models using a mixture of local optimization and exhaustive search procedures. Real-world experiments with a team of UAV and UGVs show that our method can achieve centimeter-level global localization accuracy. We also show that our method consistently outperforms random positioning of UGVs by a large margin, with as much as a 90% reduction in position and angular estimation error. Our method can tolerate a significant amount of random as well as non-stochastic measurement noise. This indicates its potential for reliable state estimation on board size, weight, and power (SWaP) constrained UAVs. This work enables robust localization in perceptually-challenged GPS-denied environments, thus paving the road for large-scale multi-robot navigation and mapping.