Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
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DOI：
10.1007/978-1-4757-3437-9_24
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发表时间：
2000-06
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影响因子：
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通讯作者：
A. Doucet;Nando de Freitas;Kevin P. Murphy;Stuart J. Russell
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文献类型：
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作者：
A. Doucet;Nando de Freitas;Kevin P. Murphy;Stuart J. Russell
高维状态空间中的粒子滤波可能效率低下，因为需要大量样本来表示后验。提高采样技术效率的标准技术是通过分析边缘化一些变量来减小状态空间的大小；这称为 Rao-Blackwell 化（Casella 和 Robert 1996）。将这两种技术结合起来就形成了 Rao-Blackwellized 粒子过滤 (RBPF) (Doucet 1998, Doucet, de Freitas, Murphy and Russell 2000)。在本章中，我们解释 RBPF，讨论何时可以使用它，并给出其在移动机器人地图学习问题中的应用的详细示例，该机器人具有非常大（约 2100）的离散状态空间。
Particle filtering in high dimensional state-spaces can be inefficient because a large number of samples is needed to represent the posterior. A standard technique to increase the efficiency of sampling techniques is to reduce the size of the state space by marginalizing out some of the variables analytically; this is called Rao-Blackwellisation (Casella and Robert 1996). Combining these two techniques results in Rao-Blackwellised particle filtering (RBPF) (Doucet 1998, Doucet, de Freitas, Murphy and Russell 2000). In this chapter, we explain RBPF, discuss when it can be used, and give a detailed example of its application to the problem of map learning for a mobile robot, which has a very large (~ 2100) discrete state space.