Estimating Spatially-Dependent GPS Errors Using a Swarm of Robots

2025-06-24

AI Summary

This work addresses the challenge of modeling static, spatially heterogeneous GPS errors in complex urban environments such as urban canyons. To this end, we propose a multi-robot collaborative perception and active learning framework. Methodologically, it fuses GPS, ranging, and bearing measurements; integrates a State Bias Estimation (SBE) algorithm with Sparse Gaussian Process (SGP) regression; and couples them with an information-driven Active Path Planning (IPP) strategy to enable intelligent exploration of high-value regions. Our key contribution is the first closed-loop integration of SBE, SGP, and IPP for GPS error field modeling—significantly improving both sampling efficiency and modeling accuracy. Simulation results demonstrate that, under identical sampling budgets, the proposed method reduces root-mean-square error (RMSE) in error-field reconstruction by 42% and increases information gain by a factor of 3.1 compared to open-loop baselines.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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.

Problem

• Estimating spatially-varying GPS errors in dynamic environments
• Developing algorithm to correct GPS biases using robot swarms
• Optimizing data collection for accurate positional bias mapping

Innovation

• State Bias Estimation Algorithm for GPS errors
• Gaussian Process Regression for bias modeling
• Informative Path Planning for data collection