차량 자세 추정을 위한 6축 관성센서와 속도계의 센서 융합 기법
Sensor Fusion for Vehicle Attitude Estimation using Six-axis Inertial Sensor and Speedometer
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This research propose a vehicle attitude estimation method using sensor fusion of speedometer and six-axis inertial sensor. External acceleration generated in a dynamic environment such as high-speed movement of a vehicle causes a considerable error in the attitude angle measured by the accelerometer. The external acceleration is estimated using velocity data obtained from speedometers mounted on most vehicles. The vehicle velocity is decomposed into three vector components using the angular velocity and attitude angle measured by the inertial sensor in the previous time step. The attitude angle estimated by the speedometer and the accelerometer is used to correct the error of the gyro sensor in the Kalman filter. In order to verify the performance of the proposed algorithm, experiments on a scenario of rapid acceleration/deceleration of a truck in a straight section and a scenario of high-speed driving on a long-distance highway are conducted.
Abstract
This research propose a vehicle attitude estimation method using sensor fusion of speedometer and six-axis inertial sensor. External acceleration generated in a dynamic environment such as high-speed movement of a vehicle causes a considerable error in the attitude angle measured by the accelerometer. The external acceleration is estimated using velocity data obtained from speedometers mounted on most vehicles. The vehicle velocity is decomposed into three vector components using the angular velocity and attitude angle measured by the inertial sensor in the previous time step. The attitude angle estimated by the speedometer and the accelerometer is used to correct the error of the gyro sensor in the Kalman filter. In order to verify the performance of the proposed algorithm, experiments on a scenario of rapid acceleration/deceleration of a truck in a straight section and a scenario of high-speed driving on a long-distance highway are conducted.
참고문헌 (14)
- S. R. Lord, "A physiological profile approach to falls risk assessment and prevention", Phys. Ther, 83, pp.37~52 (2003)
- J. Vaganay, "Mobile robot attitude estimation by fusion of inertial data", Proceedings IEEE Inte-rnational Conference on Robotics and Automation (1993)
- E. J. Lefferts, "Kalman filtering for spacecraft attitude estimation", J. Guid. Control Dyn, 5(5), pp.417~429 (1982)
- J. K. Lee, "Estimation of attitude and external acceleration using inertial sensor measurement during various dynamic conditions", IEEE Trans. Instrum. Meas, 61(8), pp.2262~2273 (2012)
- 윤자웅, "Improvement of Dynamic Respiration Monitoring Through Sensor Fusion of Accelerometer and Gyro-sensor", Journal of Electrical Engineering & Technology, 9(1), pp.334~343 (2014), 대한전기학회
- J. Borenstein, "Heuristic reduction of gyro drift for personnel tracking systems", J. Navig, 62(1), pp.41~58 (2009)
- Indirect Kalman filter for 3D attitude estimation, "N. Trawny", University of Minnesota, Dept. of Comp. Sci. & Eng, ~ (2005)
- J. Wu, "Fast complementary filter for attitude estimation using low-cost MARG sensors", IEEE Sens. J., 16(18), pp.6997~7007 (2016)
- H. Ahmed, "Accurate attitude estimation of a moving land vehicle using low-cost MEMS IMU sensors", IEEE Trans. Intell. Transp. Syst., 18(7), pp.1723~1739 (2016)
- Y. S. Suh, "Attitude estimation adaptively compensating external acceleration", JSME Int. J, C.-Mech. Sy, 49(1), pp.172~179 (2006)
- A. M. Sabatini, "Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing", IEEE Trans. Biomed. Eng, 53(7), pp.1346~1356 (2006)
- 김주원, "GPS", IMU, IMMKF를 이용한 차량 Pitch 추정 알고리즘, pp.한국통신학회논문지, 2015[DOI]
- K. Jo, "Real-time road-slope estimation based on integration of onboard sensors with GPS using an IMMPDA filter", IEEE trans. Intell. Transp. Syst., 14(4), pp.1718~1732 (2013)
- H. Pacejka, "Tire and vehicle dynamics" (2005), Elsevier