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. © 2010 Springer Science+Business Media, LLC.

Large scale graph-based SLAM using aerial images as prior information / R., Kuemmerle; Bastian, Steder; Christian, Dornhege; Alexander, Kleiner; Grisetti, Giorgio; Wolfram, Burgard. - In: AUTONOMOUS ROBOTS. - ISSN 0929-5593. - 30:1(2011), pp. 25-39. [10.1007/s10514-010-9204-1]

Large scale graph-based SLAM using aerial images as prior information

GRISETTI, GIORGIO
;
2011

Abstract

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. © 2010 Springer Science+Business Media, LLC.
2011
aerial images; localization; mapping
01 Pubblicazione su rivista::01a Articolo in rivista
Large scale graph-based SLAM using aerial images as prior information / R., Kuemmerle; Bastian, Steder; Christian, Dornhege; Alexander, Kleiner; Grisetti, Giorgio; Wolfram, Burgard. - In: AUTONOMOUS ROBOTS. - ISSN 0929-5593. - 30:1(2011), pp. 25-39. [10.1007/s10514-010-9204-1]
File allegati a questo prodotto
File Dimensione Formato  
Kümmerle_Postprint_Large-Scale_2011.pdf

accesso aperto

Note: https://link.springer.com/article/10.1007/s10514-010-9204-1
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.27 MB
Formato Adobe PDF
1.27 MB Adobe PDF
Kümmerle_Large-Scale_2011.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.24 MB
Formato Adobe PDF
2.24 MB Adobe PDF   Contatta l'autore
VE_2011_11573-137104.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.24 MB
Formato Adobe PDF
2.24 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/137104
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 107
  • ???jsp.display-item.citation.isi??? 89
social impact