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Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach

Most existing algorithms in mobile robotics consider a kinematic robot model for the the Simultaneous Localization and Mapping (SLAM) problem. However, in the case of autonomous vehicles, because of the increase in the mass and velocities, a kinematic model is not enough to characterize some physica...

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Autores principales: Vial, Pau, Puig, Vicenç
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656438/
https://www.ncbi.nlm.nih.gov/pubmed/36365908
http://dx.doi.org/10.3390/s22218211
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author Vial, Pau
Puig, Vicenç
author_facet Vial, Pau
Puig, Vicenç
author_sort Vial, Pau
collection PubMed
description Most existing algorithms in mobile robotics consider a kinematic robot model for the the Simultaneous Localization and Mapping (SLAM) problem. However, in the case of autonomous vehicles, because of the increase in the mass and velocities, a kinematic model is not enough to characterize some physical effects as, e.g., the slip angle. For this reason, when applying SLAM to autonomous vehicles, the model used should be augmented considering both kinematic and dynamic behaviours. The inclusion of dynamic behaviour implies that nonlinearities of the vehicle model are most important. For this reason, classical observation techniques based on the the linearization of the system model around the operation point, such as the well known Extended Kalman Filter (EKF), should be improved. Consequently, new techniques of advanced control must be introduced to more efficiently treat the nonlinearities of the involved models. The Linear Parameter Varying (LPV) technique allows working with nonlinear models, making a pseudolinear representation, and establishing systematic methodologies to design state estimation schemes applying several specifications. In recent years, it has been proved in many applications that this advanced technique is very useful in real applications, and it has been already implemented in a wide variety of application fields. In this article, we present a SLAM-based localization system for an autonomous vehicle considering the dynamic behaviour using LPV techniques. Comparison results are provided to show how our proposal outperforms classical observation techniques based on model linearization.
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spelling pubmed-96564382022-11-15 Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach Vial, Pau Puig, Vicenç Sensors (Basel) Article Most existing algorithms in mobile robotics consider a kinematic robot model for the the Simultaneous Localization and Mapping (SLAM) problem. However, in the case of autonomous vehicles, because of the increase in the mass and velocities, a kinematic model is not enough to characterize some physical effects as, e.g., the slip angle. For this reason, when applying SLAM to autonomous vehicles, the model used should be augmented considering both kinematic and dynamic behaviours. The inclusion of dynamic behaviour implies that nonlinearities of the vehicle model are most important. For this reason, classical observation techniques based on the the linearization of the system model around the operation point, such as the well known Extended Kalman Filter (EKF), should be improved. Consequently, new techniques of advanced control must be introduced to more efficiently treat the nonlinearities of the involved models. The Linear Parameter Varying (LPV) technique allows working with nonlinear models, making a pseudolinear representation, and establishing systematic methodologies to design state estimation schemes applying several specifications. In recent years, it has been proved in many applications that this advanced technique is very useful in real applications, and it has been already implemented in a wide variety of application fields. In this article, we present a SLAM-based localization system for an autonomous vehicle considering the dynamic behaviour using LPV techniques. Comparison results are provided to show how our proposal outperforms classical observation techniques based on model linearization. MDPI 2022-10-26 /pmc/articles/PMC9656438/ /pubmed/36365908 http://dx.doi.org/10.3390/s22218211 Text en © 2022 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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vial, Pau
Puig, Vicenç
Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach
title Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach
title_full Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach
title_fullStr Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach
title_full_unstemmed Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach
title_short Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach
title_sort kinematic/dynamic slam for autonomous vehicles using the linear parameter varying approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656438/
https://www.ncbi.nlm.nih.gov/pubmed/36365908
http://dx.doi.org/10.3390/s22218211
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