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...
| मुख्य लेखकों: | , , , , |
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| स्वरूप: | लेख |
| भाषा: | English |
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MDPI AG
2021-09-01
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| श्रृंखला: | Sensors |
| विषय: | |
| ऑनलाइन पहुंच: | https://www.mdpi.com/1424-8220/21/18/6307 |
| _version_ | 1827681263455043584 |
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| author | Marco Caruso Angelo Maria Sabatini Marco Knaflitz Ugo Della Croce Andrea Cereatti |
| author_facet | Marco Caruso Angelo Maria Sabatini Marco Knaflitz Ugo Della Croce Andrea Cereatti |
| author_sort | Marco Caruso |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T07:13:43Z |
| format | Article |
| id | doaj.art-aedd5e5a66d74e3bacf93b52ae8e639c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:43Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aedd5e5a66d74e3bacf93b52ae8e639c2023-11-22T15:14:50ZengMDPI AGSensors1424-82202021-09-012118630710.3390/s21186307Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic SensingMarco Caruso0Angelo Maria Sabatini1Marco Knaflitz2Ugo Della Croce3Andrea Cereatti4PolitoBIOMed Lab—Biomedical Engineering Lab, Politecnico di Torino, 10129 Torino, ItalyDepartment of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, ItalyPolitoBIOMed Lab—Biomedical Engineering Lab, Politecnico di Torino, 10129 Torino, ItalyDepartment of Biomedical Sciences, University of Sassari, 07100 Sassari, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, ItalyThe 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.https://www.mdpi.com/1424-8220/21/18/6307orientation estimationsensor fusionMIMUfilter parameter tuningkalman filtercomplementary filter |
| spellingShingle | Marco Caruso Angelo Maria Sabatini Marco Knaflitz Ugo Della Croce Andrea Cereatti Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing Sensors orientation estimation sensor fusion MIMU filter parameter tuning kalman filter complementary filter |
| 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 | orientation estimation sensor fusion MIMU filter parameter tuning kalman filter complementary filter |
| url | https://www.mdpi.com/1424-8220/21/18/6307 |
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