This paper addresses the problem of multi-sensor fusion and estimation for a system composed of several collaborative subsystems. A multi-sensor fusion approach based on the Kalman filter and the covariance intersection algorithm is proposed. Moreover, centralized and distributed architectures are presented and discussed—the breakdown of calculation burdens on each system component is determined. The purpose is to help in the choice of the best fusion architecture for a system composed of several collaborative subsystems, especially systems with a large number of sensors. Finally, the approach is experimentally illustrated in the context of collaborative mobile robotics. A numerical study is provided to illustrate the efficiency of each proposed architecture. Compared to the centralized architecture, the partially distributed architecture showed good stability and low requirements on the communication capacity and computing speed of the system.

本文解决了由多个协作子系统组成的系统的多传感器融合和估计问题。提出了一种基于卡尔曼滤波和协方差相交算法的多传感器融合方法。此外，提出并讨论了集中式和分布式体系结构-确定了每个系统组件上的计算负担的细目分类。目的是帮助为由多个协作子系统组成的系统（尤其是具有大量传感器的系统）选择最佳融合架构。最后，在协作移动机器人技术的上下文中实验性地说明了该方法。提供了一个数值研究来说明每个提议的体系结构的效率。与集中式架构相比，