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Large Scale Graph-based SLAM using Aerial Images as Prior Information
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2011 (English)In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 30, no 1, 25-39Article in journal (Refereed) Published
Abstract [en]

The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.

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Robotics
Identifiers
urn:nbn:se:liu:diva-72526 (URN)10.1007/s10514-010-9204-1 (DOI)oai:DiVA.org:liu-72526 (OAI)diva2:459942 (DiVA)
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Artificial Intelligence & Integrated Computer Systems
Available from2011-11-29 Created:2011-11-28 Last updated:2011-11-30Bibliographically approved

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Kümmerle, R.Steder, B.Dornhege, C.Kleiner, AlexanderGrisetti, G.Burgard, W.
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University of Freiburg
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Autonomous Robots
Robotics

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