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Author:
Kümmerle, R. (University of Freiburg)
Steder, B. (University of Freiburg)
Dornhege, C. (University of Freiburg)
Kleiner, Alexander (University of Freiburg)
Grisetti, G. (University of Freiburg)
Burgard, W. (University of Freiburg)
Title:
Large Scale Graph-based SLAM using Aerial Images as Prior Information
Publication type:
Article in journal (Refereed)
Language:
English
Publisher: Springer
Status:
Published
In:
Autonomous Robots(ISSN 0929-5593)(EISSN 1573-7527)
Volume:
30
Issue:
1
Pages:
25-39
Year of publ.:
2011
URI:
urn:nbn:se:liu:diva-72526
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72526
Subject category:
Robotics
Project:
Artificial Intelligence & Integrated Computer Systems
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.

Available from:
2011-11-29
Created:
2011-11-28
Last updated:
2011-11-30
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