Many robotic applications need an accurate, robust, and fast estimation of finger pose. We present a novel finger pose estimation method using a motion capture system. The method combines a system identification stage and a state tracking stage in a unified framework. The system identification stage develops an accurate model of a finger, and the state tracking stage tracks the finger pose with the extended Kalman filter (EKF) algorithm based on the model obtained in the system identification stage. The algorithm is validated by simulation, and experiments with a human subject and a robotic finger. The experimental results show that the method can robustly estimate the finger pose at a high frequency (greater than 1 kHz) in the presence of measurement noise, occlusion of markers, and fast movement.
Accurate, Robust, and Real-Time Pose Estimation of Finger
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 31, 2013; final manuscript received July 30, 2014; published online October 21, 2014. Assoc. Editor: Jongeun Choi.
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Yun, Y., Agarwal, P., and Deshpande, A. D. (October 21, 2014). "Accurate, Robust, and Real-Time Pose Estimation of Finger." ASME. J. Dyn. Sys., Meas., Control. March 2015; 137(3): 034505. https://doi.org/10.1115/1.4028162
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