Collaborative Multi-Robot Localization in Natural Terrain

Abstract

This paper presents a novel filter architecture that allows a team of vehicles to collaboratively localize using Terrain Relative Navigation (TRN). The work explores several causes of measurement correlation that preclude the use of traditional estimators, and proposes an estimator structure that eliminates one source of measurement correlation while properly incorporating others through the use of Covariance Intersection. The result is a consistent estimator that is able to augment proven TRN techniques with multi-robot information, significantly improving localization for vehicles in uninformative terrain. The approach is demonstrated using field data from an Autonomous Underwater Vehicle (AUV) navigating with TRN in Monterey Bay and simulated inter-vehicle range measurements. In addition, a Monte Carlo simulation was used to quantify the algorithm's performance on one example mission. Monte Carlo results show that a vehicle operating in uninformative terrain has 62% lower localization error when fusing range measurements to two converged AUVs than it would using standard TRN.