DIeSEL: DIstributed SElf-Localization of a network of underwater vehicles
It addresses the problem of localization in GPS-denied environments for teams of autonomous underwater vehicles, offering a distributed solution with theoretical guarantees.
This paper proposes a distributed self-localization algorithm for underwater vehicle networks using pairwise ranges and velocity, achieving provably convergent and accurate positioning that outperforms the centralized extended Kalman filter.
How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and limited interaction with neighbors? This paper addresses this problem from an optimization point of view: we directly optimize the nonconvex maximum-likelihood estimator in the presence of range measurements contaminated with Gaussian noise, and we obtain a provably convergent, accurate and distributed positioning algorithm that outperforms the extended Kalman filter, a standard centralized solution for this problem.