Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach
The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solut...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/5/1159 |
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| author | Fabian Girrbach Jeroen D. Hol Giovanni Bellusci Moritz Diehl |
| author_facet | Fabian Girrbach Jeroen D. Hol Giovanni Bellusci Moritz Diehl |
| author_sort | Fabian Girrbach |
| collection | DOAJ |
| description | The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. |
| first_indexed | 2024-04-11T18:24:42Z |
| format | Article |
| id | doaj.art-eb17aa514ef24bcab1ea3b32d152248b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:24:42Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eb17aa514ef24bcab1ea3b32d152248b2022-12-22T04:09:41ZengMDPI AGSensors1424-82202017-05-01175115910.3390/s17051159s17051159Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon ApproachFabian Girrbach0Jeroen D. Hol1Giovanni Bellusci2Moritz Diehl3Xsens Technologies B.V., Enschede 7521 PR, The NetherlandsXsens Technologies B.V., Enschede 7521 PR, The NetherlandsXsens Technologies B.V., Enschede 7521 PR, The NetherlandsDepartment of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, GermanyThe rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.http://www.mdpi.com/1424-8220/17/5/1159multi-sensor fusionstate estimationmoving horizon estimationnonlinear optimizationinertial navigation |
| spellingShingle | Fabian Girrbach Jeroen D. Hol Giovanni Bellusci Moritz Diehl Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach Sensors multi-sensor fusion state estimation moving horizon estimation nonlinear optimization inertial navigation |
| title | Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach |
| title_full | Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach |
| title_fullStr | Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach |
| title_full_unstemmed | Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach |
| title_short | Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach |
| title_sort | optimization based sensor fusion of gnss and imu using a moving horizon approach |
| topic | multi-sensor fusion state estimation moving horizon estimation nonlinear optimization inertial navigation |
| url | http://www.mdpi.com/1424-8220/17/5/1159 |
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