관절체에 고정된 관성 센서의 위치 및 자세 보정 기법
Pose Calibration of Inertial Measurement Units on Joint-Constrained Rigid Bodies
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
모션 캡처 장치는 자연스러운 인체 동작을 생성하는 것을 용이하게 하여 영화, 컴퓨터 게임, 컴퓨터 애니메이션 등 여러 분야에서 폭넓게 사용되고 있다. 그 중 관성 센서를 활용한 모션 캡처 장치는 보다 널리 사용되고 있는 광학 모션 캡처 장비에 비해 소요 공간과 비용 측면에서 이점을 가지고 있으나 비교적 높은 노이즈로 인해 측정 결과의 정밀도가 떨어지는 단점이 있다. 특히 관성 센서에 포함되어 중력 방향을 계측하는 가속도 센서는 센서의 선형 가속 운동으로 인해 중력 방향의 계측 정밀도가 떨어지는 문제를 갖는다. 본 논문에서는 관절체에 부착된 센서의 자세 측정 정확도를 높이기 위해 가속도 센서에서 선형 가속도 성분을 제거하는 기법을 제안한다. 아울러 센서가 부착되어 있는 관절체의 회전축 및 센서의 부착 위치를 보정하는 기법을 소개한다. 이 보정 기법은 관성 센서가 관절체의 임의의 위치와 방향으로 부착되는 것을 가능하게 한다.
Abstract
A motion capture system is widely used in movies, computer game, and computer animation industries because it allows for creating realistic human motions efficiently. The inertial motion capture system has several advantages over more popular vision-based systems in terms of the required space and cost. However, it suffers from low accuracy due to the relatively high noise levels of the inertial sensors. In particular, the accelerometer used for measuring gravity direction loses the accuracy when the sensor is moving with non-zero linear acceleration. In this paper, we propose a method to remove the linear acceleration component from the accelerometer data in order to improve the accuracy of measuring gravity direction. In addition, we develop a simple method to calibrate the joint axis of a link to which an inertial sensor belongs as well as the position of a sensor with respect to the link. The calibration enables attaching inertial sensors in an arbitrary position and orientation with respect to a link.
참고문헌 (19)
- D. Roetenberg, "Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors", Xsens Motion Technologies BV, Tech. Rep (2009)
- D. Vlasic, "Practical motion capture in everyday surroundings", ACM Transactions on Graphics (TOG), 26(3), pp.35~ (2007)
- T. Shiratori, "Motion capture from body-mounted cameras", ACM Transactions on Graphics (TOG), 30(4), pp.31~ (2011)
- J. Tautges, "Motion reconstruction using sparse accelerometerdata", ACM Transactions on Graphics (TOG), 30(3), pp.18~ (2011)
- H. Kim, "Reconstructing Whole-Body Motions with Wrist Trajectories", Graphical Models, 75(6), pp.328~345 (2013)
- 이지홍, "인체 동작 인식을 위한 가속도 센서의 신호 처리", 제어로봇시스템학회 논문지, 5(8), pp.961~968 (1999)
- D. Roetenberg, "Ambulatory position and orientation tracking fusing magnetic and inertial sensing", IEEE Transactions on Biomedical Engineering, 54(5), pp.883~890 (2007)
- R. Zhu, "A real-time articulated human motion tracking using tri-axisinertial", magnetic sensors package, 2004, pp.12(2), 295~302
- H. Zhou, "Use of multiple wearable inertial sensors in upper limb motion tracking", Medical Engineering and Physics, 30(1), pp.123~133 (2008)
- J. L. Marins, "An extended kalman filter for quaternion-based orientation estimation using marg sensors", IEEE Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE, 2001, pp.4, 2003~2011
- X. Yun, "An improved quaternion-based kalman filter for real-time tracking of rigid body orientation", IEEE Intelligent Robots and Systems, 2003. Proceedings. 2003 IEEE, 2003, pp.2, 1074~1079
- A.M. Sabatini, "Quaternion-based extended kalman filter for determining orientation by inertial and magnetic sensing", IEEE Transactions on Biomedical Engineering, 53(7), pp.1346~1356 (2006)
- X. Yun, "Design, implementation, and experimental results of a quaternion-based kalman filter for human body motion tracking", IEEE Transactions on Robotics, 22(6), pp.1216~1227 (2006)
- X. Yun, "A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurement", IEEE Transactions on Instrumentation and Measurement, 57(3), pp.638~650 (2008)
- J. Calusdian, "Adaptive-gain complementary filter of inertial and magnetic data for orientation estimation", IEEE International Conference onRobotics and Automation (ICRA), pp.1916~1922 (2011)
- Y. Tian, "An adaptive-gain complementary filter for real-time human motion tracking with marg sensors in free-living environments", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(2), pp.254~264 (2013)
- S. OH. Madgwick, "Estimation of imu and marg orientation using a gradient descent algorithm", 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), pp.1~7 (2011)
- Z. Zhang, "Hierarchical information fusion for human upper limb motion capture", 12th International Conference on Information Fusion, 2009. FUSION'09, pp.1704~1711 (2009)
- Z. Zhang, "Wearable sensors for 3d upper limb motion modeling and ubiquitous estimation", Journal of Control Theory and Applications, 9(1), pp.10~17 (2011)