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Collaborative mergi...
Collaborative merging of radio SLAM maps in view of crowd-sourced data acquisition and big data
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- Batstone, Kenneth (author)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Oskarsson, Magnus (author)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Astrom, Kalle (author)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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Fred, Ana (editor)
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De Marsico, Maria (editor)
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di Baja, Gabriella Sanniti (editor)
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(creator_code:org_t)
- SCITEPRESS - Science and Technology Publications, 2019
- 2019
- English 7 s.
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In: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and Technology Publications. - 9789897583513 ; , s. 807-813
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http://dx.doi.org/10... (free)
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Big Data
- Crowdsourced
- Radio Slam
- Toa Self-calibration
Publication and Content Type
- kon (subject category)
- ref (subject category)
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