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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6479366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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