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A Belief Propagation Algorithm for Multipath-Based SLAM with Multiple Map Features : A mmWave MIMO Application

Li, Xuhong LU ; Cai, Xuesong LU ; Leitinger, Erik LU and Tufvesson, Fredrik LU orcid (2024) 59th Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024 In 2024 IEEE International Conference on Communications Workshops, ICC Workshops 2024 p.269-275
Abstract

In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that con-tinuously adapts mulitiple map feature (MF) models describing specularly reflected multi path components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobile agent's state including position and orientation. The Bayesian model is represented by a factor graph enabling the use of belief propagation (BP) for efficient computation of the marginal posterior distributions. The algorithm also exploits amplitude information enabling reliable detection of 'weak' MFs asso-ciated with MPCs of very low... (More)

In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that con-tinuously adapts mulitiple map feature (MF) models describing specularly reflected multi path components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobile agent's state including position and orientation. The Bayesian model is represented by a factor graph enabling the use of belief propagation (BP) for efficient computation of the marginal posterior distributions. The algorithm also exploits amplitude information enabling reliable detection of 'weak' MFs asso-ciated with MPCs of very low signal-to-noise ratios (SNRs). The performance of the proposed algorithm is evaluated using real millimeter-wave (mm Wave) multiple-input-multiple-output (MIMO) measurements with single base station setup. Results demonstrate the excellent localization and mapping performance of the proposed algorithm in challenging dynamic outdoor scenarios.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2024 IEEE International Conference on Communications Workshops, ICC Workshops 2024
series title
2024 IEEE International Conference on Communications Workshops, ICC Workshops 2024
editor
Valenti, Matthew ; Reed, David and Torres, Melissa
pages
7 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
59th Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024
conference location
Denver, United States
conference dates
2024-06-09 - 2024-06-13
external identifiers
  • scopus:85198573354
ISBN
9798350304053
DOI
10.1109/ICCWorkshops59551.2024.10615625
language
English
LU publication?
yes
id
aa4eb40d-ecfb-4532-90e7-03b53b032a24
date added to LUP
2025-01-15 14:09:01
date last changed
2025-01-15 14:09:14
@inproceedings{aa4eb40d-ecfb-4532-90e7-03b53b032a24,
  abstract     = {{<p>In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that con-tinuously adapts mulitiple map feature (MF) models describing specularly reflected multi path components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobile agent's state including position and orientation. The Bayesian model is represented by a factor graph enabling the use of belief propagation (BP) for efficient computation of the marginal posterior distributions. The algorithm also exploits amplitude information enabling reliable detection of 'weak' MFs asso-ciated with MPCs of very low signal-to-noise ratios (SNRs). The performance of the proposed algorithm is evaluated using real millimeter-wave (mm Wave) multiple-input-multiple-output (MIMO) measurements with single base station setup. Results demonstrate the excellent localization and mapping performance of the proposed algorithm in challenging dynamic outdoor scenarios.</p>}},
  author       = {{Li, Xuhong and Cai, Xuesong and Leitinger, Erik and Tufvesson, Fredrik}},
  booktitle    = {{2024 IEEE International Conference on Communications Workshops, ICC Workshops 2024}},
  editor       = {{Valenti, Matthew and Reed, David and Torres, Melissa}},
  isbn         = {{9798350304053}},
  language     = {{eng}},
  pages        = {{269--275}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{2024 IEEE International Conference on Communications Workshops, ICC Workshops 2024}},
  title        = {{A Belief Propagation Algorithm for Multipath-Based SLAM with Multiple Map Features : A mmWave MIMO Application}},
  url          = {{http://dx.doi.org/10.1109/ICCWorkshops59551.2024.10615625}},
  doi          = {{10.1109/ICCWorkshops59551.2024.10615625}},
  year         = {{2024}},
}