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Positioning with Ma...
Positioning with Map Matching using Deep Neural Networks
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- Bergkvist, Hannes (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Sony, R&D Center Europe, Lund, Sweden
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- Davidsson, Paul (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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- Exner, Peter (författare)
- Sony, R&D Center Europe, Lund, Sweden
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(creator_code:org_t)
- 2021-08-09
- 2020
- Engelska.
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Ingår i: MobiQuitous '20. - New York, NY, USA : Association for Computing Machinery (ACM).
- Relaterad länk:
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https://mau.diva-por... (primary) (Raw object)
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Deep neural networks for positioning can improve accuracy by adapting to inhomogeneous environments. However, they are still susceptible to noisy data, often resulting in invalid positions. A related task, map matching, can be used for reducing geographical invalid positions by aligning observations to a model of the real world. In this paper, we propose an approach for positioning, enhanced with map matching, within a single deep neural network model. We introduce a novel way of reducing the number of invalid position estimates by adding map information to the input of the model and using a map-based loss function. Evaluating on real-world Received Signal Strength Indicator data from an asset tracking application, we show that our approach gives both increased position accuracy and a decrease of one order of magnitude in the number of invalid positions.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Deep neural networks
- Localization
- Positioning
- Map matching
- Loss function
- Adaptation
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)