@article{Blanco2006,
abstract = {Recently, hybrid maps that combine metric and topological world information$\backslash$nhave been proposed as a powerful representation of mobile robot environments.$\backslash$nAmong others, these maps are of special interest for efficiently$\backslash$nmanaging large-scale environments, and for accurate localization.$\backslash$nFor achieving that, local geometric maps are stored in the nodes$\backslash$nof a graph-based global map. In this paper we present a novel approach$\backslash$nfor automatically obtaining those local maps from observations. The$\backslash$nmethod considers the space sensed in each observation as a node of$\backslash$na graph with arcs representing the space overlap between observations.$\backslash$nThe recursive partition (cut) of this graph produces groups of strongly$\backslash$nconnected nodes from which consistent local maps for accurate localization$\backslash$nare derived. The proposed partition technique is well-grounded in$\backslash$nthe spectral graph theory of, and it is formulated for any type of$\backslash$nsensor observation. We depict an implementation for grouping 2D laser$\backslash$nscans, and show experimental results with real data that demonstrate$\backslash$nthe performance of the method},
author = {Blanco, Jose Luis and Gonzalez, Javier and Fernandez-Madrigal, Juan Antonio},
doi = {10.1109/ROBOT.2006.1641810},
file = {:Users/nick/Documents/Mendeley Desktop/blanco2006.pdf:pdf},
isbn = {0780395069},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
keywords = {Graph partitioning,Map building,Mobile robots,Topological maps},
number = {May},
pages = {818--823},
title = {{Consistent observation grouping for generating metric-topological maps that improves robot localization}},
volume = {2006},
year = {2006}
}
