Consistent observation grouping for generating metric-topological maps that improves robot localization Export

@inproceedings{blanco-etal-icra06, abstract = {Recently, hybrid maps that combine metric and topological world information have been proposed as a powerful representation of mobile robot environments. Among others, these maps are of special interest for efficiently managing large-scale environments, and for accurate localization. For achieving that, local geometric maps are stored in the nodes of a graph-based global map. In this paper we present a novel approach for automatically obtaining those local maps from observations. The method considers the space sensed in each observation as a node of a graph with arcs representing the space overlap between observations. The recursive partition (cut) of this graph produces groups of strongly connected nodes from which consistent local maps for accurate localization are derived. The proposed partition technique is well-grounded in the spectral graph theory of, and it is formulated for any type of sensor observation. We depict an implementation for grouping 2D laser scans, and show experimental results with real data that demonstrate the performance of the method}, author = {Blanco, J. L. and Gonzalez, J. and Fernandez-Madrigal, J. A.}, booktitle = {Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2006.}, citeulike-article-id = {4325502}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ROBOT.2006.1641810}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1641810}, doi = {10.1109/ROBOT.2006.1641810}, journal = {Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on}, keywords = {graph, normcut, slam}, pages = {818--823}, posted-at = {2009-04-16 12:10:36}, priority = {2}, title = {Consistent observation grouping for generating metric-topological maps that improves robot localization}, url = {http://dx.doi.org/10.1109/ROBOT.2006.1641810}, year = {2006} }

TY - CONF ID - blanco-etal-icra06 L3 - citeulike-article-id:4325502 N2 - Recently, hybrid maps that combine metric and topological world information have been proposed as a powerful representation of mobile robot environments. Among others, these maps are of special interest for efficiently managing large-scale environments, and for accurate localization. For achieving that, local geometric maps are stored in the nodes of a graph-based global map. In this paper we present a novel approach for automatically obtaining those local maps from observations. The method considers the space sensed in each observation as a node of a graph with arcs representing the space overlap between observations. The recursive partition (cut) of this graph produces groups of strongly connected nodes from which consistent local maps for accurate localization are derived. The proposed partition technique is well-grounded in the spectral graph theory of, and it is formulated for any type of sensor observation. We depict an implementation for grouping 2D laser scans, and show experimental results with real data that demonstrate the performance of the method JF - Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on EP - 823 TI - Consistent observation grouping for generating metric-topological maps that improves robot localization T2 - Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2006. SP - 818 KW - graph KW - normcut KW - slam AU - Blanco, JL AU - Gonzalez, J AU - Fernandez-Madrigal, JA PY - 2006/// UR - http://dx.doi.org/10.1109/ROBOT.2006.1641810 ER -