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Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step h...

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Detalles Bibliográficos
Autores principales: Harder, Dorian, Shoushtari, Hossein, Sternberg, Harald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105771/
https://www.ncbi.nlm.nih.gov/pubmed/35590980
http://dx.doi.org/10.3390/s22093289
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author Harder, Dorian
Shoushtari, Hossein
Sternberg, Harald
author_facet Harder, Dorian
Shoushtari, Hossein
Sternberg, Harald
author_sort Harder, Dorian
collection PubMed
description Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure.
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spelling pubmed-91057712022-05-14 Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis Harder, Dorian Shoushtari, Hossein Sternberg, Harald Sensors (Basel) Article Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure. MDPI 2022-04-25 /pmc/articles/PMC9105771/ /pubmed/35590980 http://dx.doi.org/10.3390/s22093289 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Harder, Dorian
Shoushtari, Hossein
Sternberg, Harald
Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis
title Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis
title_full Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis
title_fullStr Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis
title_full_unstemmed Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis
title_short Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis
title_sort real-time map matching with a backtracking particle filter using geospatial analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105771/
https://www.ncbi.nlm.nih.gov/pubmed/35590980
http://dx.doi.org/10.3390/s22093289
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