Estimating Spatially-Dependent GPS Errors Using a Swarm of Robots

TL;DR

This paper presents a method for estimating spatially-varying GPS errors using a drone swarm, combining bias estimation, Gaussian process modeling, and informative path planning to improve localization accuracy in challenging environments.

Contribution

It introduces a novel combined approach of bias estimation, Gaussian process regression, and informative path planning for spatial GPS error mapping with robot swarms.

Findings

The SBE algorithm accurately estimates GPS biases across the environment.

The IPP strategy outperforms open-loop data collection in information gain.

Simulation results demonstrate improved GPS error modeling and localization.

Abstract

External factors, including urban canyons and adversarial interference, can lead to Global Positioning System (GPS) inaccuracies that vary as a function of the position in the environment. This study addresses the challenge of estimating a static, spatially-varying error function using a team of robots. We introduce a State Bias Estimation Algorithm (SBE) whose purpose is to estimate the GPS biases. The central idea is to use sensed estimates of the range and bearing to the other robots in the team to estimate changes in bias across the environment. A set of drones moves in a 2D environment, each sampling data from GPS, range, and bearing sensors. The biases calculated by the SBE at estimated positions are used to train a Gaussian Process Regression (GPR) model. We use a Sparse Gaussian process-based Informative Path Planning (IPP) algorithm that identifies high-value regions of the environment for data collection. The swarm plans paths that maximize information gain in each iteration, further refining their understanding of the environment's positional bias landscape. We evaluated SBE and IPP in simulation and compared the IPP methodology to an open-loop strategy.