论文标题
从占用网格中提取语义室内图
Extracting Semantic Indoor Maps from Occupancy Grids
论文作者
论文摘要
对于在现实，不受约束的情况下运行的任何自主系统的主要挑战是管理现实世界的复杂性和不确定性。尽管尚不清楚人类和其他高等动物如何掌握这些问题，但显而易见的是，抽象起着重要的作用。抽象概念的使用允许在更高级别上定义系统行为。在本文中，我们着重于室内环境的语义映射。我们提出了一种使用贝叶斯推理从典型的网格图中提取抽象的平面图的方法。该过程的结果是通过抽象概念定义的环境的概率生成模型。它非常适合更高级别的推理和沟通目的。我们使用现实世界数据证明了该方法的有效性。
The primary challenge for any autonomous system operating in realistic, rather unconstrained scenarios is to manage the complexity and uncertainty of the real world. While it is unclear how exactly humans and other higher animals master these problems, it seems evident, that abstraction plays an important role. The use of abstract concepts allows to define the system behavior on higher levels. In this paper we focus on the semantic mapping of indoor environments. We propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result of this procedure is a probabilistic generative model of the environment defined over abstract concepts. It is well suited for higher-level reasoning and communication purposes. We demonstrate the effectiveness of the approach using real-world data.
