We argue the case for Gaussian Belief Propagation (GBP) as a strong algorithmic framework for the distributed, generic and incremental probabilistic estimation we need in Spatial AI as we aim at high performance smart robots and devices which operate within the constraints of real products. Processor hardware is changing rapidly, and GBP has the right character to take advantage of highly distributed processing and storage while estimating global quantities, as well as great flexibility. We present a detailed tutorial on GBP, relating to the standard factor graph formulation used in robotics and computer vision, and give several simulation examples with code which demonstrate its properties.

翻译：我们认为高斯信仰传播(GBP)是一个强有力的算法框架,用于空间AI中我们所需要的分布式、通用和递增概率估算,因为我们的目标是高性能的智能机器人和在实际产品限制下运行的装置。 加工硬件正在迅速变化,英镑在估计全球数量的同时利用高度分布式的处理和储存以及极大的灵活性是正当的。 我们对英镑提出了详细的辅导,涉及机器人和计算机视觉中使用的标准要素图配方,并提供了几个模拟示例,以显示其特性。