Cargando…
Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing
The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune para...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473403/ https://www.ncbi.nlm.nih.gov/pubmed/34577514 http://dx.doi.org/10.3390/s21186307 |
_version_ | 1784574983193755648 |
---|---|
author | Caruso, Marco Sabatini, Angelo Maria Knaflitz, Marco Della Croce, Ugo Cereatti, Andrea |
author_facet | Caruso, Marco Sabatini, Angelo Maria Knaflitz, Marco Della Croce, Ugo Cereatti, Andrea |
author_sort | Caruso, Marco |
collection | PubMed |
description | The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online. |
format | Online Article Text |
id | pubmed-8473403 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84734032021-09-28 Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing Caruso, Marco Sabatini, Angelo Maria Knaflitz, Marco Della Croce, Ugo Cereatti, Andrea Sensors (Basel) Article The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online. MDPI 2021-09-21 /pmc/articles/PMC8473403/ /pubmed/34577514 http://dx.doi.org/10.3390/s21186307 Text en © 2021 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 Caruso, Marco Sabatini, Angelo Maria Knaflitz, Marco Della Croce, Ugo Cereatti, Andrea Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing |
title | Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing |
title_full | Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing |
title_fullStr | Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing |
title_full_unstemmed | Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing |
title_short | Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing |
title_sort | extension of the rigid-constraint method for the heuristic suboptimal parameter tuning to ten sensor fusion algorithms using inertial and magnetic sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473403/ https://www.ncbi.nlm.nih.gov/pubmed/34577514 http://dx.doi.org/10.3390/s21186307 |
work_keys_str_mv | AT carusomarco extensionoftherigidconstraintmethodfortheheuristicsuboptimalparametertuningtotensensorfusionalgorithmsusinginertialandmagneticsensing AT sabatiniangelomaria extensionoftherigidconstraintmethodfortheheuristicsuboptimalparametertuningtotensensorfusionalgorithmsusinginertialandmagneticsensing AT knaflitzmarco extensionoftherigidconstraintmethodfortheheuristicsuboptimalparametertuningtotensensorfusionalgorithmsusinginertialandmagneticsensing AT dellacroceugo extensionoftherigidconstraintmethodfortheheuristicsuboptimalparametertuningtotensensorfusionalgorithmsusinginertialandmagneticsensing AT cereattiandrea extensionoftherigidconstraintmethodfortheheuristicsuboptimalparametertuningtotensensorfusionalgorithmsusinginertialandmagneticsensing |