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

Institute of Signal Processing and Speech Communication (4420)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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.

Original language	English
Title of host publication	15th European Conference on Antennas and Propagation, EuCAP 2021
Place of Publication	Dusseldorf, Germany
Pages	1-5
Number of pages	5
ISBN (Electronic)	9788831299022
DOIs	https://doi.org/10.23919/EuCAP51087.2021.9411320
Publication status	Published - 22 Mar 2021
Event	15th European Conference on Antennas and Propagation: EucAP 2021 - Virtuell, Düsseldorf, Germany Duration: 22 Mar 2021 → 26 Mar 2021

Publication series

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

Conference

Conference	15th European Conference on Antennas and Propagation
Abbreviated title	EucAP 2021
Country/Territory	Germany
City	Virtuell, Düsseldorf
Period	22/03/21 → 26/03/21

ASJC Scopus subject areas

Signal Processing
Instrumentation
Computer Networks and Communications

Fields of Expertise

Information, Communication & Computing

Access to Document

10.23919/EuCAP51087.2021.9411320

Cite this

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. p. 1-5 9411320 (15th European Conference on Antennas and Propagation, EuCAP 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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, pp. 1-5, 15th European Conference on Antennas and Propagation, Virtuell, Düsseldorf, Germany, 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.",

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

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ER -

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

Abstract

Publication series

Conference

ASJC Scopus subject areas

Fields of Expertise

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CD-Laboratory for Location-aware Electronic Systems

Cite this

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

Abstract

Publication series

Conference

ASJC Scopus subject areas

Fields of Expertise

Access to Document

Other files and links

Fingerprint

Projects

CD-Laboratory for Location-aware Electronic Systems

Cite this