Controlling virtual cameras based on a robust model-free pose acquisition technique
IEEE Transactions on Multimedia
Visual control through the trifocal tensor for nonholonomic robots
Robotics and Autonomous Systems
Pose-estimation-based visual servoing for differential-drive robots using the 1D trifocal tensor
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
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In this paper, an innovative extended Kalman filter (EKF) algorithm for pose tracking using the trifocal tensor is proposed. In the EKF, a constant-velocity motion model is used as the dynamic system, and the trifocal-tensor constraint is incorporated into the measurement model. The proposed method has the advantages of those structure- and-motion-based approaches in that the pose sequence can be computed with no prior information on the scene structure. It also has the strengths of those model-based algorithms in which no updating of the three-dimensional (3-D) structure is necessary in the computation. This results in a stable, accurate, and efficient algorithm. Experimental results show that the proposed approach outperformed other existing EKFs that tackle the same problem. An extension to the pose-tracking algorithm has been made to demonstrate the application of the trifocal constraint to fast recursive 3-D structure recovery