Efficient Merging of Maps and Detection of Changes
(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.
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- author
- Flood, Gabrielle LU ; Gillsjö, David LU ; Heyden, Anders LU and Åström, Kalle LU
- organization
- publishing date
- 2019
- 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
- 2024-09-18 02:54:15
@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}}, }