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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:
Conference paper (Refereed)
Language:
English
In:
Proceedings of Robotics Science and Systems (RSS)
Conference:
Proceedings of Robotics Science and Systems (RSS)
Publisher: MIT Press
Year of publ.:
2009
URI:
urn:nbn:se:liu:diva-72536
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72536
Subject category:
Robotics
Project:
Artificial Intelligence & Integrated Computer Systems
Abstract(en) :

To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. 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, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment 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:
2011-11-29
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