Structured synthesis and compression of semantic human sensor models for Bayesian estimation

Nicholas Sweet, N. Ahmed
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引用次数: 16

Abstract

We consider the problem of fusing human-generated semantic `soft sensor' data with conventional `hard sensor' data to augment Bayesian state estimators. This requires modeling semantic soft data via generalized continuous-to-discrete softmax likelihood functions, which can theoretically model semantic descriptions of any dynamic state space. This paper addresses two important related issues for deploying these models in practical applications. First, a general solution to the data-free likelihood synthesis problem is provided. This allows for easy embedding of contextual constraints and other relevant a priori information within generalized softmax models, without resorting to expensive non-convex optimization procedures for parameter estimation with sparse data. This result is then used to derive strategies for combining multiple semantic human observation models into `compressed' likelihood functions for fast batch data fusion. The proposed methods are demonstrated on a human-robot target search application.
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基于贝叶斯估计的语义人体传感器模型的结构化合成与压缩
我们考虑将人工生成的语义“软传感器”数据与传统的“硬传感器”数据融合以增强贝叶斯状态估计器的问题。这需要通过广义的连续到离散的softmax似然函数来建模语义软数据,理论上可以建模任何动态状态空间的语义描述。本文讨论了在实际应用中部署这些模型的两个重要相关问题。首先,给出了无数据似然综合问题的一般解法。这允许在广义softmax模型中轻松嵌入上下文约束和其他相关的先验信息,而无需借助于昂贵的非凸优化过程来对稀疏数据进行参数估计。然后,该结果用于导出将多个语义人类观察模型组合为“压缩”似然函数的策略,以实现快速批量数据融合。并在一个人机目标搜索应用中进行了验证。
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