Abstract:

In the field of muhi-robot cooperative localization,it is necessary to model accurate dynamic equations and observation equations, which need nonlinea   rity and non-Gauss systems. Several nonlinearity algorithms are applied in this realm. There are mainly two kinds. One is the extended Kalman filter （E EKF）, which makes a local linearization to a nonlinear system, so the Kalman filter （KF） can be utilized indirectly to filter and estimate. The other r is the sequential Monte Carlo method on point mass, which is also called the particle filter（PF）. In this paper, we introduce an improved particle f  filter,namely the Gauss-Hermite particle filter（GHPF）. We lay our stress on comparing the effect of these three algorithms, which are applied in the r realm of multi-robot cooperative localization.

Key words: （cooperative localization, extended Kalman filter, particle filter, Gau ss-Hermite particle filter）