[Robotics Institute Seminar]Conditional Random Fields for Labeling Tasks in Robotics

Date: Nov. 30, 2007
Title: Conditional Random Fields for Labeling Tasks in Robotics
Speaker: Dieter Fox, University of Washington, Seattle

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 high-dimensional feature vectors.

This is joint work with Bertrand Douillard, Stephen Friedman, Benson Limketkai, Lin Liao, and Fabio Ramos.