Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration
Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/2/337 |
| _version_ | 1797415930654883840 |
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| author | Máté Fazekas Péter Gáspár Balázs Németh |
| author_facet | Máté Fazekas Péter Gáspár Balázs Németh |
| author_sort | Máté Fazekas |
| collection | DOAJ |
| description | Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions. |
| first_indexed | 2024-03-09T05:56:32Z |
| format | Article |
| id | doaj.art-b1f08b00963b436e874b8b4ccd016613 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:56:32Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b1f08b00963b436e874b8b4ccd0166132023-12-03T12:13:20ZengMDPI AGSensors1424-82202021-01-0121233710.3390/s21020337Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics IntegrationMáté Fazekas0Péter Gáspár1Balázs Németh2Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, HungarySystems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), H-1111 Budapest, HungarySystems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), H-1111 Budapest, HungaryLocalization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.https://www.mdpi.com/1424-8220/21/2/337positioningwheel odometrycalibrationsensor fusionGauss–Newton regressionKalman-filtering |
| spellingShingle | Máté Fazekas Péter Gáspár Balázs Németh Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration Sensors positioning wheel odometry calibration sensor fusion Gauss–Newton regression Kalman-filtering |
| title | Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration |
| title_full | Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration |
| title_fullStr | Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration |
| title_full_unstemmed | Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration |
| title_short | Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration |
| title_sort | calibration and improvement of an odometry model with dynamic wheel and lateral dynamics integration |
| topic | positioning wheel odometry calibration sensor fusion Gauss–Newton regression Kalman-filtering |
| url | https://www.mdpi.com/1424-8220/21/2/337 |
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