Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Efficient Merging of Maps and Detection of Changes

Flood, Gabrielle LU ; Gillsjö, David LU orcid ; Heyden, Anders LU orcid and Åström, Kalle LU orcid (2019) 21st Scandinavian Conference on Image Analysis, SCIA 2019 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11482 LNCS. p.348-360
Abstract

With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This... (More)

With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This makes it possible to remove inconsistent parts before merging the maps. The methods are tested on both simulated and real data, using both sensor data from radio sensors and from cameras.

(Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Change detection, Collaborative SLAM, Map merging, SfM
host publication
Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Felsberg, Michael ; Forssén, Per-Erik ; Unger, Jonas and Sintorn, Ida-Maria
volume
11482 LNCS
pages
13 pages
publisher
Springer
conference name
21st Scandinavian Conference on Image Analysis, SCIA 2019
conference location
Norrköping, Sweden
conference dates
2019-06-11 - 2019-06-13
external identifiers
  • scopus:85066889639
ISSN
1611-3349
0302-9743
ISBN
9783030202040
DOI
10.1007/978-3-030-20205-7_29
language
English
LU publication?
yes
id
5fb8162a-618d-44f2-ad8a-5429f00c4f6d
date added to LUP
2019-06-19 14:23:00
date last changed
2024-04-30 13:48:08
@inproceedings{5fb8162a-618d-44f2-ad8a-5429f00c4f6d,
  abstract     = {{<p>With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This makes it possible to remove inconsistent parts before merging the maps. The methods are tested on both simulated and real data, using both sensor data from radio sensors and from cameras.</p>}},
  author       = {{Flood, Gabrielle and Gillsjö, David and Heyden, Anders and Åström, Kalle}},
  booktitle    = {{Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings}},
  editor       = {{Felsberg, Michael and Forssén, Per-Erik and Unger, Jonas and Sintorn, Ida-Maria}},
  isbn         = {{9783030202040}},
  issn         = {{1611-3349}},
  keywords     = {{Change detection; Collaborative SLAM; Map merging; SfM}},
  language     = {{eng}},
  pages        = {{348--360}},
  publisher    = {{Springer}},
  series       = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  title        = {{Efficient Merging of Maps and Detection of Changes}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-20205-7_29}},
  doi          = {{10.1007/978-3-030-20205-7_29}},
  volume       = {{11482 LNCS}},
  year         = {{2019}},
}