基于高斯-施密特粒子滤波器的多机器人协同定位

多机器人协同定位需对各个机器人的运动模型和观测模型精确建模,需要运用非线性、非高斯系统。已经应用于本领域的各种非线性算法主要有两种：一种是扩展卡尔曼滤波算法（EKF）,它对非线性系统进行局部线性化,从而间接利用卡尔曼算法进行滤波与估算;另一种是序列蒙特卡罗算法,即粒子滤波器（PF）。本文介绍了一种改进的粒子滤波
器,即高斯-施密特粒子滤波器（GHPF）,重点比较这三种算法在多机器人协同定位领域的应用效果。

Multi-Robot Cooperative Localization Based on the Gauss-Hermite Particle Filter

In the field of multi-robot cooperative localization,it is necessary to model accurate dynamic equations and observation equations, which need nonlinearity and non-Gauss systems. Several nonlinearity algorithms are applied in this realm. There are mainly two kinds. One is the extended Kalman filter (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 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 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 realm of multi-robot cooperative localization.