Rao-Blackwellized Posterior Linearization Backward SLAM

Ángel F. García-Fernández; Roland Hostettler; Simo Särkkä

doi:10.1109/TVT.2019.2903569

Rao-Blackwellized Posterior Linearization Backward SLAM

Ángel F. García-Fernández, Roland Hostettler, Simo Särkkä

Research output: Contribution to journal › Article › Scientific › peer-review

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Abstract

This paper proposes the posterior linearisation backward simultaneous localisation and mapping (PLB-SLAM) algorithm for batch SLAM problems. Based on motion and landmark measurements, we aim to estimate the trajectory of the mobile agent and the landmark positions using an approximate Rao-Blackwellised Monte Carlo solution, as in FastSLAM. PLB-SLAM improves the accuracy of current FastSLAM solutions due to two key aspects: smoothing of the trajectory distribution via backward trajectory simulation and the use of iterated posterior linearisation to obtain Gaussian approximations of the distribution of the landmarks. PLB-SLAM is assessed via numerical simulations and real experiments for indoor localisation and mapping of radio beacons using a smartphone, Bluetooth beacons, and Wi-Fi access points.

Original language	English
Article number	8662708
Pages (from-to)	4734-4747
Number of pages	14
Journal	IEEE Transactions on Vehicular Technology
Volume	68
Issue number	5
Early online date	2019
DOIs	https://doi.org/10.1109/TVT.2019.2903569
Publication status	Published - 1 May 2019
MoE publication type	A1 Journal article-refereed

Keywords

Simultaneous localisation and mapping
Back-ward simulation
Posterior linearisation
Rao-Blackwellisation
Bluetooth beacons
Wi-Fi access points
Smartphone
Rao-Blackwellization
Backward simulation
Simultaneous localization and mapping
Posterior linearization

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10.1109/TVT.2019.2903569

ELEC_Garcia-Fernandez_etal_Rao-Blackwellised-Posterior_IEEETraVeTec_acceptedauthormanuscriptAccepted author manuscript, 7.35 MB

Cite this

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title = "Rao-Blackwellized Posterior Linearization Backward SLAM",

abstract = "This paper proposes the posterior linearisation backward simultaneous localisation and mapping (PLB-SLAM) algorithm for batch SLAM problems. Based on motion and landmark measurements, we aim to estimate the trajectory of the mobile agent and the landmark positions using an approximate Rao-Blackwellised Monte Carlo solution, as in FastSLAM. PLB-SLAM improves the accuracy of current FastSLAM solutions due to two key aspects: smoothing of the trajectory distribution via backward trajectory simulation and the use of iterated posterior linearisation to obtain Gaussian approximations of the distribution of the landmarks. PLB-SLAM is assessed via numerical simulations and real experiments for indoor localisation and mapping of radio beacons using a smartphone, Bluetooth beacons, and Wi-Fi access points. ",

keywords = "Simultaneous localisation and mapping, Back-ward simulation, Posterior linearisation, Rao-Blackwellisation, Bluetooth beacons, Wi-Fi access points, Smartphone, Rao-Blackwellization, Backward simulation, Simultaneous localization and mapping, Posterior linearization",

author = "Garc{\'i}a-Fern{\'a}ndez, {{\'A}ngel F.} and Roland Hostettler and Simo S{\"a}rkk{\"a}",

year = "2019",

month = may,

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doi = "10.1109/TVT.2019.2903569",

language = "English",

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journal = "IEEE Transactions on Vehicular Technology",

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AU - García-Fernández, Ángel F.

AU - Hostettler, Roland

AU - Särkkä, Simo

PY - 2019/5/1

Y1 - 2019/5/1

N2 - This paper proposes the posterior linearisation backward simultaneous localisation and mapping (PLB-SLAM) algorithm for batch SLAM problems. Based on motion and landmark measurements, we aim to estimate the trajectory of the mobile agent and the landmark positions using an approximate Rao-Blackwellised Monte Carlo solution, as in FastSLAM. PLB-SLAM improves the accuracy of current FastSLAM solutions due to two key aspects: smoothing of the trajectory distribution via backward trajectory simulation and the use of iterated posterior linearisation to obtain Gaussian approximations of the distribution of the landmarks. PLB-SLAM is assessed via numerical simulations and real experiments for indoor localisation and mapping of radio beacons using a smartphone, Bluetooth beacons, and Wi-Fi access points.

AB - This paper proposes the posterior linearisation backward simultaneous localisation and mapping (PLB-SLAM) algorithm for batch SLAM problems. Based on motion and landmark measurements, we aim to estimate the trajectory of the mobile agent and the landmark positions using an approximate Rao-Blackwellised Monte Carlo solution, as in FastSLAM. PLB-SLAM improves the accuracy of current FastSLAM solutions due to two key aspects: smoothing of the trajectory distribution via backward trajectory simulation and the use of iterated posterior linearisation to obtain Gaussian approximations of the distribution of the landmarks. PLB-SLAM is assessed via numerical simulations and real experiments for indoor localisation and mapping of radio beacons using a smartphone, Bluetooth beacons, and Wi-Fi access points.

KW - Simultaneous localisation and mapping

KW - Back-ward simulation

KW - Posterior linearisation

KW - Rao-Blackwellisation

KW - Bluetooth beacons

KW - Wi-Fi access points

KW - Smartphone

KW - Rao-Blackwellization

KW - Backward simulation

KW - Simultaneous localization and mapping

KW - Posterior linearization

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JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

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ER -

Rao-Blackwellized Posterior Linearization Backward SLAM

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Keywords

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Crowdsourced mapping of the environment- multimodal real-time SLAM via combinedinertial, optical, and magnetic sensoring

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Rao-Blackwellized Posterior Linearization Backward SLAM

Abstract

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Crowdsourced mapping of the environment- multimodal real-time SLAM via combinedinertial, optical, and magnetic sensoring

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