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A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing

Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and...

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Detalles Bibliográficos
Autor principal: Sommer, Volker
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5949030/
https://www.ncbi.nlm.nih.gov/pubmed/29673205
http://dx.doi.org/10.3390/s18041236
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author Sommer, Volker
author_facet Sommer, Volker
author_sort Sommer, Volker
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description Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in the case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, in particular, the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating quickly and reliably the covariance matrix dependent on just a few comprehensible and easily-obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a priori knowledge of the specific measurement noise. By means of extensive simulations, the performance and robustness of the new model in comparison to existing approaches is shown.
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spelling pubmed-59490302018-05-17 A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing Sommer, Volker Sensors (Basel) Article Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in the case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, in particular, the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating quickly and reliably the covariance matrix dependent on just a few comprehensible and easily-obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a priori knowledge of the specific measurement noise. By means of extensive simulations, the performance and robustness of the new model in comparison to existing approaches is shown. MDPI 2018-04-17 /pmc/articles/PMC5949030/ /pubmed/29673205 http://dx.doi.org/10.3390/s18041236 Text en © 2018 by the author. 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/).
spellingShingle Article
Sommer, Volker
A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing
title A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing
title_full A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing
title_fullStr A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing
title_full_unstemmed A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing
title_short A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing
title_sort closed-form error model of straight lines for improved data association and sensor fusing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5949030/
https://www.ncbi.nlm.nih.gov/pubmed/29673205
http://dx.doi.org/10.3390/s18041236
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