论文标题
基于马尔可夫逻辑网络和数据驱动的MCMC的语义室内映射的可推广知识框架
A Generalizable Knowledge Framework for Semantic Indoor Mapping Based on Markov Logic Networks and Data Driven MCMC
论文作者
论文摘要
在本文中，我们为数据抽象提出了一个可推广的知识框架，即使用预定义的抽象术语为输入数据找到紧凑的抽象模型。基于这些抽象的术语，智能自主系统（例如机器人）应该能够根据特定的知识库进行推论，以便它们可以更好地处理现实世界的复杂性和不确定性。我们建议通过组合Markov逻辑网络（MLN）和数据驱动的MCMC采样来实现此框架，因为前者是对不确定知识进行建模的强大工具，后者提供了一种从未知复杂分布中绘制样本的有效方法。此外，我们详细说明了如何将此框架适应某个任务，尤其是语义机器人映射。基于MLN，我们将特定于任务的上下文知识作为描述性软规则。现实世界数据和模拟数据的实验证实了我们框架的有用性。
In this paper, we propose a generalizable knowledge framework for data abstraction, i.e. finding compact abstract model for input data using predefined abstract terms. Based on these abstract terms, intelligent autonomous systems, such as a robot, should be able to make inference according to specific knowledge base, so that they can better handle the complexity and uncertainty of the real world. We propose to realize this framework by combining Markov logic networks (MLNs) and data driven MCMC sampling, because the former are a powerful tool for modelling uncertain knowledge and the latter provides an efficient way to draw samples from unknown complex distributions. Furthermore, we show in detail how to adapt this framework to a certain task, in particular, semantic robot mapping. Based on MLNs, we formulate task-specific context knowledge as descriptive soft rules. Experiments on real world data and simulated data confirm the usefulness of our framework.
