Lab Meeting January 8th, 2008 (Yu-Hsiang): CRF-Matching: Conditional Random Fields for Feature-Based Scan Matching

Author :
Fabio Ramos,Dieter Fox,Hugh Durrant-Whyte

Abstract :
Matching laser range scans observed at differentpoints in time is a crucial component of many robotics tasks,including mobile robot localization and mapping. While existingtechniques such as the Iterative Closest Point (ICP) algorithmperform well under many circumstances, they often fail when theinitial estimate of the offset between scans is highly uncertain.This paper presents a novel approach to 2D laser scan matching.CRF-Matching generates a Condition Random Field (CRF) toreason about the joint association between the measurementsof the two scans. The approach is able to consider arbitraryshape and appearance features in order to match laser scans.The model parameters are learned from labeled training data.Inference is performed efficiently using loopy belief propagation.Experiments using data collected by a car navigating throughurban environments show that CRF-Matching is able to reliablyand efficiently match laser scans even when no a priori knowledgeabout their offset is given. They additionally demonstrate that ourapproach can seamlessly integrate camera information, therebyfurther improving performance.

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