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The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. (Automatic Control)
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control. (Automatic Control)ORCID iD: 0000-0003-3270-171X
2017 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, no 2, 60-69 p.Article in journal (Refereed) Published
Abstract [en]

Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 34, no 2, 60-69 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-135786DOI: 10.1109/MSP.2016.2637418ISI: 000397574300008Scopus ID: 2-s2.0-85015356723OAI: oai:DiVA.org:liu-135786DiVA: diva2:1083734
Projects
Wallenberg Autonomous Systems Program
Note

Funding agencies: Wallenberg Autonomous Systems Program

Available from: 2017-03-22 Created: 2017-03-22 Last updated: 2017-04-21Bibliographically approved

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Karlsson, RickardGustafsson, Fredrik
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CiteExportLink to record
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Citation style
  • apa
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More styles
Language
  • de-DE
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  • Other locale
More languages
Output format
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