CRF-Matching: Conditional
Random Fields for Feature-Based Scan Matching
Proc. of Robotics: Science & Systems (RSS), 2007
Abstract
Matching laser range scans observed at different points in time is a
crucial component of many robotics tasks, including mobile robot
localization and mapping. While existing techniques such as the
Iterative Closest Point (ICP) algorithm perform well under many
circumstances, they often fail when the initial 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) to reason about the joint
association between the measurements of the two scans. The
approach is able to consider arbitrary shape 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 through urban environments show
that CRF-Matching is able to reliably and efficiently match laser
scans even when no a priori knowledge about their offset is
given. They additionally demonstrate that our approach can
seamlessly integrate camera information, thereby further improving
performance.