Efficient Merging of Maps and Detection of Changes

Gabrielle Flood; David Gillsjö; Anders Heyden; Kalle Åström

doi:10.1007/978-3-030-20205-7_29

Efficient Merging of Maps and Detection of Changes

Gabrielle Flood, David Gillsjö, Anders Heyden, Kalle Åström

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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 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.

Original language	English
Title of host publication	Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
Editors	Michael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
Publisher	Springer
Pages	348-360
Number of pages	13
ISBN (Print)	9783030202040
DOIs	https://doi.org/10.1007/978-3-030-20205-7_29
Publication status	Published - 2019
Event	21st Scandinavian Conference on Image Analysis, SCIA 2019 - Norrköping, Sweden Duration: 2019 Jun 11 → 2019 Jun 13

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11482 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st Scandinavian Conference on Image Analysis, SCIA 2019
Country/Territory	Sweden
City	Norrköping
Period	2019/06/11 → 2019/06/13

Subject classification (UKÄ)

Computer Vision and Robotics (Autonomous Systems)

Free keywords

Change detection
Collaborative SLAM
Map merging
SfM

Access to Document

10.1007/978-3-030-20205-7_29

2 Doctoral Thesis (compilation)

Applications in Monocular Computer Vision using Geometry and Learning: Map Merging, 3D Reconstruction and Detection of Geometric Primitives
Gillsjö, D., 2023, Lund: Lund University / Centre for Mathematical Sciences /LTH.
Research output: Thesis › Doctoral Thesis (compilation)

Open Access
File
Mapping and Merging Using Sound and Vision: Automatic Calibration and Map Fusion with Statistical Deformations
Flood, G., 2021 Dec 10, Lund University / Centre for Mathematical Sciences /LTH. 238 p.
Research output: Thesis › Doctoral Thesis (compilation)

Open Access
File

Cite this

Flood, G., Gillsjö, D., Heyden, A., & Åström, K. (2019). Efficient Merging of Maps and Detection of Changes. In M. Felsberg, P.-E. Forssén, J. Unger, & I.-M. Sintorn (Eds.), Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings (pp. 348-360). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11482 LNCS). Springer. https://doi.org/10.1007/978-3-030-20205-7_29

Flood, Gabrielle ; Gillsjö, David ; Heyden, Anders et al. / Efficient Merging of Maps and Detection of Changes. Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. editor / Michael Felsberg ; Per-Erik Forssén ; Jonas Unger ; Ida-Maria Sintorn. Springer, 2019. pp. 348-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Efficient Merging of Maps and Detection of Changes",

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 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.",

keywords = "Change detection, Collaborative SLAM, Map merging, SfM",

author = "Gabrielle Flood and David Gillsj{\"o} and Anders Heyden and Kalle {\AA}str{\"o}m",

year = "2019",

doi = "10.1007/978-3-030-20205-7_29",

language = "English",

isbn = "9783030202040",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

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Flood, G , Gillsjö, D , Heyden, A & Åström, K 2019, Efficient Merging of Maps and Detection of Changes. in M Felsberg, P-E Forssén, J Unger & I-M Sintorn (eds), Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11482 LNCS, Springer, pp. 348-360, 21st Scandinavian Conference on Image Analysis, SCIA 2019, Norrköping, Sweden, 2019/06/11. https://doi.org/10.1007/978-3-030-20205-7_29

Efficient Merging of Maps and Detection of Changes. / Flood, Gabrielle ; Gillsjö, David ; Heyden, Anders et al.
Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. ed. / Michael Felsberg; Per-Erik Forssén; Jonas Unger; Ida-Maria Sintorn. Springer, 2019. p. 348-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11482 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Efficient Merging of Maps and Detection of Changes

AU - Flood, Gabrielle

AU - Gillsjö, David

AU - Heyden, Anders

AU - Åström, Kalle

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Change detection

KW - Collaborative SLAM

KW - Map merging

KW - SfM

U2 - 10.1007/978-3-030-20205-7_29

DO - 10.1007/978-3-030-20205-7_29

M3 - Paper in conference proceeding

AN - SCOPUS:85066889639

SN - 9783030202040

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 348

EP - 360

BT - Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings

A2 - Felsberg, Michael

A2 - Forssén, Per-Erik

A2 - Unger, Jonas

A2 - Sintorn, Ida-Maria

PB - Springer

T2 - 21st Scandinavian Conference on Image Analysis, SCIA 2019

Y2 - 11 June 2019 through 13 June 2019

ER -

Flood G , Gillsjö D , Heyden A , Åström K. Efficient Merging of Maps and Detection of Changes. In Felsberg M, Forssén PE, Unger J, Sintorn IM, editors, Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. Springer. 2019. p. 348-360. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-20205-7_29

Efficient Merging of Maps and Detection of Changes

Abstract

Publication series

Conference

Subject classification (UKÄ)

Free keywords

Access to Document

Fingerprint

Research output

Applications in Monocular Computer Vision using Geometry and Learning: Map Merging, 3D Reconstruction and Detection of Geometric Primitives

Mapping and Merging Using Sound and Vision: Automatic Calibration and Map Fusion with Statistical Deformations

Cite this