좌표변환 기반의 두 자세 정렬 기법 비교
Comparison between Two Coordinate Transformation-Based Orientation Alignment Methods
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Inertial measurement units (IMUs) are widely used for wearable motion-capturing systems in the fields of biomechanics and robotics. When the IMUs are combined with optical motion sensors (hereafter, OPTs) for their complementary capabilities, it is necessary to alignthe coordinate system orientations between the IMU and OPT. In this study, we compare the application of two coordinate trans-formation-based orientation alignment methods between two coordinate systems. The first method (M1) applies angular velocity coor- dinate transformation, while the other method (M2) applies gyroscopic angle coordinate transformation. In M1 and M2, the angularvelocities and angles, respectively, are acquired during random movement for a least-square algorithm to determine the alignment matrixbetween the two coordinate systems. The performance of each method is evaluated under various conditions according to the type ofmotion during measurement, number of data points, amount of noise, and the alignment matrix. The results show that M1 is free from drift errors, while drift errors are present in most cases where M2 is applied. Thus, this study indicates that M1 has a far superior per- formance than M2 for the alignment of IMU and OPT coordinate systems for motion analysis.
Abstract
Inertial measurement units (IMUs) are widely used for wearable motion-capturing systems in the fields of biomechanics and robotics. When the IMUs are combined with optical motion sensors (hereafter, OPTs) for their complementary capabilities, it is necessary to alignthe coordinate system orientations between the IMU and OPT. In this study, we compare the application of two coordinate trans-formation-based orientation alignment methods between two coordinate systems. The first method (M1) applies angular velocity coor- dinate transformation, while the other method (M2) applies gyroscopic angle coordinate transformation. In M1 and M2, the angularvelocities and angles, respectively, are acquired during random movement for a least-square algorithm to determine the alignment matrixbetween the two coordinate systems. The performance of each method is evaluated under various conditions according to the type ofmotion during measurement, number of data points, amount of noise, and the alignment matrix. The results show that M1 is free from drift errors, while drift errors are present in most cases where M2 is applied. Thus, this study indicates that M1 has a far superior per- formance than M2 for the alignment of IMU and OPT coordinate systems for motion analysis.
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