마그네토미터 보정 데이터의 이상치 제거가 타원체 피팅기반 보정성능에 미치는 영향 (Effects of Outlier Elimination in Magnetometer Calibration Data on the Performance of Ellipsoid Fitting-based Calibration)

서지정보

· 발행기관 : 제어·로봇·시스템학회
· 수록지 정보 : 제어.로봇.시스템학회 논문지 / 30권 / 12호 / 1373 ~ 1379페이지
· 저자명 : 김지훈, 이창준, 이정근

초록

A magnetometer should be calibrated to correct errors such as offset, scale factor, misalignment soft iron distortion, and hard iron distortion so as to achieve accurate azimuth estimation. An outlier elimination method has been recently proposed to improve calibration performance by eliminating the outliers of calibration data. In this method, it is assumed that calibration data is collected in a uniform distribution to form a sphere. However, considering the actual data collection process, it is difficult to guarantee uniformly distributed data. Thus, this study investigates the effects of outlier elimination on the performance of ellipsoid fitting-based magnetometer calibration. In this study, the calibration performances before and after outlier elimination were compared for the following three cases: uniformly distributed data with insignificant outliers (Case 1), uniformly distributed data with significant outliers (Case 2), and nonuniformly distributed data (Case 3). In addition, the calibration performances were compared according to a threshold for detecting outliers. The results of this study showed that outlier elimination changed the azimuth estimation performance by −0.002°, 0.711°, and −2.588° for Cases 1–3, respectively, in terms of root mean squared error. Overall, these results indicate that outlier elimination is effective for uniformly distributed data with significant outliers; however, it causes performance degradation for nonuniformly distributed data. Therefore, it is essential to examine the distribution of calibration data before applying outlier elimination.

영어초록

A magnetometer should be calibrated to correct errors such as offset, scale factor, misalignment soft iron distortion, and hard iron distortion so as to achieve accurate azimuth estimation. An outlier elimination method has been recently proposed to improve calibration performance by eliminating the outliers of calibration data. In this method, it is assumed that calibration data is collected in a uniform distribution to form a sphere. However, considering the actual data collection process, it is difficult to guarantee uniformly distributed data. Thus, this study investigates the effects of outlier elimination on the performance of ellipsoid fitting-based magnetometer calibration. In this study, the calibration performances before and after outlier elimination were compared for the following three cases: uniformly distributed data with insignificant outliers (Case 1), uniformly distributed data with significant outliers (Case 2), and nonuniformly distributed data (Case 3). In addition, the calibration performances were compared according to a threshold for detecting outliers. The results of this study showed that outlier elimination changed the azimuth estimation performance by −0.002°, 0.711°, and −2.588° for Cases 1–3, respectively, in terms of root mean squared error. Overall, these results indicate that outlier elimination is effective for uniformly distributed data with significant outliers; however, it causes performance degradation for nonuniformly distributed data. Therefore, it is essential to examine the distribution of calibration data before applying outlier elimination.

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