Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver
Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactiv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/6/864 |
| _version_ | 1798040283875639296 |
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| author | Chan-Gun Lee Nhu-Ngoc Dao Seonmin Jang Deokhwan Kim Yonghun Kim Sungrae Cho |
| author_facet | Chan-Gun Lee Nhu-Ngoc Dao Seonmin Jang Deokhwan Kim Yonghun Kim Sungrae Cho |
| author_sort | Chan-Gun Lee |
| collection | DOAJ |
| description | Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3 × 10−1 to 5.3 × 10−7, respectively. |
| first_indexed | 2024-04-11T22:05:23Z |
| format | Article |
| id | doaj.art-a16a3a2ca87645ef921b759b74597b76 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:05:23Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a16a3a2ca87645ef921b759b74597b762022-12-22T04:00:44ZengMDPI AGSensors1424-82202016-06-0116686410.3390/s16060864s16060864Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion DriverChan-Gun Lee0Nhu-Ngoc Dao1Seonmin Jang2Deokhwan Kim3Yonghun Kim4Sungrae Cho5School of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, KoreaDepartment of Vehicle Components, LG Electronics, Seoul 073-36, KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, KoreaSensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3 × 10−1 to 5.3 × 10−7, respectively.http://www.mdpi.com/1424-8220/16/6/864sensor fusionindirect Kalman filteraccuracy improvementgyro drift correction |
| spellingShingle | Chan-Gun Lee Nhu-Ngoc Dao Seonmin Jang Deokhwan Kim Yonghun Kim Sungrae Cho Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver Sensors sensor fusion indirect Kalman filter accuracy improvement gyro drift correction |
| title | Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver |
| title_full | Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver |
| title_fullStr | Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver |
| title_full_unstemmed | Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver |
| title_short | Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver |
| title_sort | gyro drift correction for an indirect kalman filter based sensor fusion driver |
| topic | sensor fusion indirect Kalman filter accuracy improvement gyro drift correction |
| url | http://www.mdpi.com/1424-8220/16/6/864 |
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