Conditional Random Fields for Semantic Mapping Tasks

Dieter Fox

Department of Computer Science & Engineering
University of Washington, Washington, USA

Abstract. Over the last decade, the mobile robotics community has developed highly efficient and robust solutions to estimation problems such as robot localization and map building. With the availability of various techniques for spatially consistent sensor integration, an important next goal is the extraction of high-level information from sensor data. Such information is often discrete, requiring techniques different from those typically applied to mapping and localization. In this talk I will describe how Conditional Random Fields (CRF) can be applied to tasks such as semantic place labeling, object recognition, and scan matching. CRFs are discriminative, undirected graphical models that were developed for labeling sequence data. Due to their ability to handle arbitrary dependencies between observation features, CRFs are extremely well suited for classification problems involving highdimensional feature vectors. However, the adequate incorporation of continuous features into CRFs is not trivial, and I will discuss a combination of boosting and CRF training that increases the effectiveness of CRFs applied to continuous data.