Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) si...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/7/3676 |
| _version_ | 1797606997340717056 |
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| author | Yuming Yin Jinhong Zhang Mengqi Guo Xiaobin Ning Yuan Wang Jianshan Lu |
| author_facet | Yuming Yin Jinhong Zhang Mengqi Guo Xiaobin Ning Yuan Wang Jianshan Lu |
| author_sort | Yuming Yin |
| collection | DOAJ |
| description | High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization. |
| first_indexed | 2024-03-11T05:25:05Z |
| format | Article |
| id | doaj.art-b3e5333da56c441ba4b47957fc546dd6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:25:05Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b3e5333da56c441ba4b47957fc546dd62023-11-17T17:36:06ZengMDPI AGSensors1424-82202023-04-01237367610.3390/s23073676Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman FilterYuming Yin0Jinhong Zhang1Mengqi Guo2Xiaobin Ning3Yuan Wang4Jianshan Lu5College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaCollege of Engineering, Beijing Forestry University, Beijing 100083, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaHigh−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization.https://www.mdpi.com/1424-8220/23/7/3676error state KalmanRTS smoothingrobust and accurate localization |
| spellingShingle | Yuming Yin Jinhong Zhang Mengqi Guo Xiaobin Ning Yuan Wang Jianshan Lu Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter Sensors error state Kalman RTS smoothing robust and accurate localization |
| title | Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter |
| title_full | Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter |
| title_fullStr | Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter |
| title_full_unstemmed | Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter |
| title_short | Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter |
| title_sort | sensor fusion of gnss and imu data for robust localization via smoothed error state kalman filter |
| topic | error state Kalman RTS smoothing robust and accurate localization |
| url | https://www.mdpi.com/1424-8220/23/7/3676 |
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