Multirobot localization with unknown variance parameters using iterated Kalman filtering

The multirobot localization problem is solved in this paper using an innovative approach related to Tikhonov regularization. We release the requirement that robots are equipped with sensors to estimate their own motion, as well as the requirement that covariance matrices describing system and measure noises are perfectly known. Robots are assumed to have a single sensor returning noisy measurements of mutual distances while they move along unknown paths. The proposed algorithm estimates online both the robots´ poses as well as the unknown covariance parameters. In addition to the classical iterations of the well known iterated Kalman filter, we include iterations that propagate an approximation of the posterior marginal densities of the unknown variances. Simulationl results provide evidence that the algorithm is capable of accurately estimating the variances online while at the same time keeping the localization error bounded.