Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models

Erik Leitinger; Stefan Grebien; Klaus Witrisal

doi:10.23919/EuCAP51087.2021.9411320

Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models

Erik Leitinger, Stefan Grebien, Klaus Witrisal

Institut für Signalverarbeitung und Sprachkommunikation (4420)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

In this paper, we present a Bayesian multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts interacting multiple models (IMM) parameters to describe the mobile agent state dynamics. The time-evolution of the IMM parameters is described by a Markov chain and the parameters are incorporated into the factor graph structure that represents the statistical structure of the SLAM problem. The proposed belief propagation (BP)-based algorithm adapts, in an online manner, to time-varying system models by jointly inferring the model parameters along with the agent and map feature states. The performance of the proposed algorithm is finally evaluating with a simulated scenario. Our numerical simulation results show that the proposed multipath-based SLAM algorithm is able to cope with strongly changing agent state dynamics.

Originalsprache	englisch
Titel	15th European Conference on Antennas and Propagation, EuCAP 2021
Erscheinungsort	Dusseldorf, Germany
Seiten	1-5
Seitenumfang	5
ISBN (elektronisch)	9788831299022
DOIs	https://doi.org/10.23919/EuCAP51087.2021.9411320
Publikationsstatus	Veröffentlicht - 22 März 2021
Veranstaltung	15th European Conference on Antennas and Propagation: EucAP 2021 - Virtuell, Düsseldorf, Deutschland Dauer: 22 März 2021 → 26 März 2021

Publikationsreihe

Name	15th European Conference on Antennas and Propagation, EuCAP 2021

Konferenz

Konferenz	15th European Conference on Antennas and Propagation
Kurztitel	EucAP 2021
Land/Gebiet	Deutschland
Ort	Virtuell, Düsseldorf
Zeitraum	22/03/21 → 26/03/21

ASJC Scopus subject areas

Signalverarbeitung
Instrumentierung
Computernetzwerke und -kommunikation

Fields of Expertise

Information, Communication & Computing

Zugriff auf Dokument

10.23919/EuCAP51087.2021.9411320

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

CD-Labor für Ortssensitive Elektronische Systeme
Witrisal, K. (Teilnehmer (Co-Investigator)), Grebien, S. J. (Teilnehmer (Co-Investigator)), Fuchs, A. (Teilnehmer (Co-Investigator)), Wilding, T. (Teilnehmer (Co-Investigator)), Venus, A. (Teilnehmer (Co-Investigator)) & Wielandner, L. (Teilnehmer (Co-Investigator))
1/01/18 → 31/12/25
Projekt: Forschungsprojekt

Dieses zitieren

Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models. / Leitinger, Erik; Grebien, Stefan; Witrisal, Klaus.
15th European Conference on Antennas and Propagation, EuCAP 2021. Dusseldorf, Germany, 2021. S. 1-5 9411320 (15th European Conference on Antennas and Propagation, EuCAP 2021).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Leitinger, E, Grebien, S & Witrisal, K 2021, Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models. in 15th European Conference on Antennas and Propagation, EuCAP 2021., 9411320, 15th European Conference on Antennas and Propagation, EuCAP 2021, Dusseldorf, Germany, S. 1-5, 15th European Conference on Antennas and Propagation, Virtuell, Düsseldorf, Deutschland, 22/03/21. https://doi.org/10.23919/EuCAP51087.2021.9411320

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title = "Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models",

abstract = "In this paper, we present a Bayesian multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts interacting multiple models (IMM) parameters to describe the mobile agent state dynamics. The time-evolution of the IMM parameters is described by a Markov chain and the parameters are incorporated into the factor graph structure that represents the statistical structure of the SLAM problem. The proposed belief propagation (BP)-based algorithm adapts, in an online manner, to time-varying system models by jointly inferring the model parameters along with the agent and map feature states. The performance of the proposed algorithm is finally evaluating with a simulated scenario. Our numerical simulation results show that the proposed multipath-based SLAM algorithm is able to cope with strongly changing agent state dynamics.",

author = "Erik Leitinger and Stefan Grebien and Klaus Witrisal",

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AB - In this paper, we present a Bayesian multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts interacting multiple models (IMM) parameters to describe the mobile agent state dynamics. The time-evolution of the IMM parameters is described by a Markov chain and the parameters are incorporated into the factor graph structure that represents the statistical structure of the SLAM problem. The proposed belief propagation (BP)-based algorithm adapts, in an online manner, to time-varying system models by jointly inferring the model parameters along with the agent and map feature states. The performance of the proposed algorithm is finally evaluating with a simulated scenario. Our numerical simulation results show that the proposed multipath-based SLAM algorithm is able to cope with strongly changing agent state dynamics.

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M3 - Conference paper

T3 - 15th European Conference on Antennas and Propagation, EuCAP 2021

SP - 1

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BT - 15th European Conference on Antennas and Propagation, EuCAP 2021

CY - Dusseldorf, Germany

T2 - 15th European Conference on Antennas and Propagation

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Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models

Abstract

Publikationsreihe

Konferenz

ASJC Scopus subject areas

Fields of Expertise

Zugriff auf Dokument

Andere Dateien und Links

Fingerprint

Projekte

CD-Labor für Ortssensitive Elektronische Systeme

Dieses zitieren