6 축 관성센서를 이용한 센서가속도 추정용 칼만필터

국문 초록 (Abstract)

가속도계의 신호는 운동체의 가속도와 다르며, 운동체의 자세가 변화하는 경우 가속도계 단독으로 센서가속도를 계측할 수 없다. 본 논문에서는 3 축 가속도계와 3 축 자이로스코프로 구성된 6 축 관성센서 신호를 바탕으로 운동체의 자세가 지속적으로 변화하는 가운데 가속도를 정확히 추정할 수 있는 칼만필터를 제안한다. 제안하는 알고리즘은 센서의 자세뿐 아니라 센서가속도가 상태벡터의 일부로 설정되어 있는 새로운 구조의 칼만필터로써, 센서 가속도를 명시적으로 정확히 구할 수 있다. 제안된 필터는 다양한 조건하에서 광학모션캡쳐시스템을 이용하여 그 정확성이 검증되었는데, 최신 Xsens MTw 센서와 동등수준의 성능이었다. 제안된 알고리즘은 6 축 관성센서를 바탕으로 운동체의 가속도 추정이 필요한 다양한 모션센서 응용분야에 적용될 수 있다.

다국어 초록 (Multilingual Abstract)

Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors.

목차 (Table of Contents)

• 초록
• Abstract
• 1. 서론
• 2. 추정 알고리즘 및 검증실험
• 3. 결과 및 고찰
• 4. 결론
• 참고문헌(References)

참고문헌 (Reference)

1 "Xsens MTw User's Manual"

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4 Suh, Y. S., "Orientation Estimation Using a Quaternion-Based Indirect Kalman Filter with Adaptive Estimation of External Acceleration" 59 (59): 3296-3305, 2010

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9 Lee, J. K., "Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions" 61 (61): 2262-2273, 2012

10 Jeong Hyun Moon, "Estimability Measures and Their Application to GPS/INS" 대한기계학회 22 (22): 905-913, 2008

11 Roetenberg, D., "Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation" 13 (13): 395-405, 2005

12 Welch, G., "An Introduction to the Kalman Filter" Dept. Comput. Sci., Univ. North Carolina Chapel Hill 1995

13 Bong-Su Cho, "A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding" 대한기계학회 25 (25): 2907-2917, 2011