Variational Bayesian data fusion of multi-class discrete observations with applications to cooperative human-robot estimation
Variational Bayesian data fusion of multi-class discrete observations with applications to cooperative human-robot estimation
复制标题
多类离散观测的变分贝叶斯数据融合及其在人机协作估计中的应用
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
M. Campbell
中科院分区：
文献类型：
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作者：
N. Ahmed;M. Campbell
提出了一种新方法，用于将传统的连续传感器观测与离散的多类别状态相关信息融合，这些信息可以由人类在许多协作人机交互问题中提供。用于连续隐藏状态和分类观察之间映射的混合似然函数通过 softmax 模型指定。尽管 softmax 模型避免了连续状态的离散化，但由于它们不可分析积分，因此实现实时数据融合具有挑战性。这里提出了一种基于变分贝叶斯 (VB) 方法的近似，以便在隐藏连续状态具有高斯 pdf 的情况下获得所需后验的快速闭式高斯解。人机联合目标定位示例说明了 VB 混合融合策略的属性和实用性，该策略也更广泛地适用于混合贝叶斯网络和混合模型中的推理。
A new method is presented for fusing conventional continuous sensor observations with discrete multi-categorical state-dependent information, which can be furnished by humans in many cooperative human-robot interaction problems. The hybrid likelihood function for mapping between continuous hidden states and categorical observations are specified via softmax models. Although softmax models avoid discretization of continuous states, they are challenging to implement for real-time data fusion since they are not analytically integrable. An approximation based on variational Bayesian (VB) methods is presented here to obtain fast closed-form Gaussian solutions to the desired posteriors in cases where the hidden continuous states have Gaussian pdfs. A joint human-robot target localization example illustrates the properties and utility of the VB hybrid fusion strategy, which also applies more generally to inference in hybrid Bayesian networks and mixture models.