Collaborative merging of radio SLAM maps in view of crowd-sourced data acquisition and big data
Batstone, Kenneth LU ; Oskarsson, Magnus LU
and Astrom, Kalle
LU
(2019)
8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019
p.807-813
- Abstract
Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, this unlocks the potential of having such devices in commonplace around factories and homes, enabling an alternative method of navigation. Therefore, indoor anchor calibration becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we present a method to fuse radio SLAM (also known as Time-Of-Arrival... (More)
Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, this unlocks the potential of having such devices in commonplace around factories and homes, enabling an alternative method of navigation. Therefore, indoor anchor calibration becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we present a method to fuse radio SLAM (also known as Time-Of-Arrival self-calibration) maps together in a linear way. In doing so we are then able to collaboratively calibrate the anchor positions in 3D to native precision of the devices. Furthermore, we introduce an automatic scheme to determine which of the maps are best to use to further improve the anchor calibration and its robustness but also show which maps could be discarded. Additionally, when a map is fused in a linear way, it is a very computationally cheap process and produces a reasonable map which is required to push for crowd-sourced data acquisition.
(Less)
- author
- Batstone, Kenneth
LU
; Oskarsson, Magnus
LU
and Astrom, Kalle
LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Big Data, Crowdsourced, Radio Slam, Toa Self-calibration
- host publication
- ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods
- editor
- Fred, Ana ; De Marsico, Maria and di Baja, Gabriella Sanniti
- pages
- 7 pages
- publisher
- SciTePress
- conference name
- 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019
- conference location
- Prague, Czech Republic
- conference dates
- 2019-02-19 - 2019-02-21
- external identifiers
-
- scopus:85064659257
- ISBN
- 9789897583513
- DOI
- 10.5220/0007574408070813
- language
- English
- LU publication?
- yes
- id
- 8e415199-e545-4d37-b9e1-a18870353082
- date added to LUP
- 2019-05-06 14:50:36
- date last changed
- 2022-04-25 23:23:05
@inproceedings{8e415199-e545-4d37-b9e1-a18870353082,
abstract = {{<p>Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, this unlocks the potential of having such devices in commonplace around factories and homes, enabling an alternative method of navigation. Therefore, indoor anchor calibration becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we present a method to fuse radio SLAM (also known as Time-Of-Arrival self-calibration) maps together in a linear way. In doing so we are then able to collaboratively calibrate the anchor positions in 3D to native precision of the devices. Furthermore, we introduce an automatic scheme to determine which of the maps are best to use to further improve the anchor calibration and its robustness but also show which maps could be discarded. Additionally, when a map is fused in a linear way, it is a very computationally cheap process and produces a reasonable map which is required to push for crowd-sourced data acquisition.</p>}},
author = {{Batstone, Kenneth and Oskarsson, Magnus and Astrom, Kalle}},
booktitle = {{ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods}},
editor = {{Fred, Ana and De Marsico, Maria and di Baja, Gabriella Sanniti}},
isbn = {{9789897583513}},
keywords = {{Big Data; Crowdsourced; Radio Slam; Toa Self-calibration}},
language = {{eng}},
pages = {{807--813}},
publisher = {{SciTePress}},
title = {{Collaborative merging of radio SLAM maps in view of crowd-sourced data acquisition and big data}},
url = {{http://dx.doi.org/10.5220/0007574408070813}},
doi = {{10.5220/0007574408070813}},
year = {{2019}},
}