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State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds

There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are...

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Autores principales: Gostar, Amirali Khodadadian, Fu, Chunyun, Chuah, Weiqin, Hossain, Mohammed Imran, Tennakoon, Ruwan, Bab-Hadiashar, Alireza, Hoseinnezhad, Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479366/
https://www.ncbi.nlm.nih.gov/pubmed/30987259
http://dx.doi.org/10.3390/s19071614
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author Gostar, Amirali Khodadadian
Fu, Chunyun
Chuah, Weiqin
Hossain, Mohammed Imran
Tennakoon, Ruwan
Bab-Hadiashar, Alireza
Hoseinnezhad, Reza
author_facet Gostar, Amirali Khodadadian
Fu, Chunyun
Chuah, Weiqin
Hossain, Mohammed Imran
Tennakoon, Ruwan
Bab-Hadiashar, Alireza
Hoseinnezhad, Reza
author_sort Gostar, Amirali Khodadadian
collection PubMed
description There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications.
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spelling pubmed-64793662019-04-29 State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds Gostar, Amirali Khodadadian Fu, Chunyun Chuah, Weiqin Hossain, Mohammed Imran Tennakoon, Ruwan Bab-Hadiashar, Alireza Hoseinnezhad, Reza Sensors (Basel) Article There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications. MDPI 2019-04-03 /pmc/articles/PMC6479366/ /pubmed/30987259 http://dx.doi.org/10.3390/s19071614 Text en © 2019 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Gostar, Amirali Khodadadian
Fu, Chunyun
Chuah, Weiqin
Hossain, Mohammed Imran
Tennakoon, Ruwan
Bab-Hadiashar, Alireza
Hoseinnezhad, Reza
State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
title State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
title_full State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
title_fullStr State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
title_full_unstemmed State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
title_short State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
title_sort state transition for statistical slam using planar features in 3d point clouds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479366/
https://www.ncbi.nlm.nih.gov/pubmed/30987259
http://dx.doi.org/10.3390/s19071614
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