论文标题
应用基于规则的上下文知识来构建室内环境的抽象语义图
Applying Rule-Based Context Knowledge to Build Abstract Semantic Maps of Indoor Environments
论文作者
论文摘要
在本文中，我们提出了一种可推广的方法，该方法使用基于规则的上下文知识进行数据抽象，系统地结合数据驱动的MCMC采样和推断。特别是，我们在为室内环境构建抽象语义图的情况下证明了我们的方法的有用性。我们系统的乘积是感知环境的参数抽象模型，不仅准确地代表了环境的几何形状，而且还提供了有价值的抽象信息，从而使高级机器人应用受益。基于预定义的抽象术语，例如类型和关系，我们将特定于任务的上下文知识定义为马尔可夫逻辑网络中的描述性规则。相应的推论结果用于构建一个旨在为语义图的解决方案空间增加合理约束的预分配。此外，通过应用语义注释的传感器模型，我们明确使用上下文信息来解释传感器数据。现实世界数据的实验显示出令人鼓舞的结果，从而确认了我们系统的有用性。
In this paper, we propose a generalizable method that systematically combines data driven MCMC samplingand inference using rule-based context knowledge for data abstraction. In particular, we demonstrate the usefulness of our method in the scenario of building abstract semantic maps for indoor environments. The product of our system is a parametric abstract model of the perceived environment that not only accurately represents the geometry of the environment but also provides valuable abstract information which benefits high-level robotic applications. Based on predefined abstract terms,such as type and relation, we define task-specific context knowledge as descriptive rules in Markov Logic Networks. The corresponding inference results are used to construct a priordistribution that aims to add reasonable constraints to the solution space of semantic maps. In addition, by applying a semantically annotated sensor model, we explicitly use context information to interpret the sensor data. Experiments on real world data show promising results and thus confirm the usefulness of our system.
