Bounded Recursive Optimization Approach for Pose Estimation in Robotic Visual Servoing
Pose estimation problem is concerned with determining position and orientation of an object in real time using the image information, and has found applications in many fields such as object recognition and robotic visual servoing. Most of vision-based pose estimation schemes are derived from extended Kalman filter, which requires that the noises obey the Gaussian distribution under known covariance. However, the statistical information in robot control may not be accurately obtained or satisfied. In this paper, a novel bounded recursive optimization approach is proposed to solve the pose estimation problem in visual serving, where the addressed noises do not provide any statistical information, and the bounds of noises are also unknown. Finally, the pose estimation simulation is conducted to show the advantages and effectiveness of the proposed approach.
KeywordsPose estimation Visual servoing Bounded recursive optimization approach
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