Predicting a moving objects position for visual servoing: theory and experiments

摘要

In order to perform visual servoing tasks in a robotic system, one is confronted with the low sampling rate of standard cameras and the time delay introduced by image processing. One way to circumvent the time-delay problem is to estimate future positions of the moving object of interest employing prediction techniques. In this work, three prediction techniques, namely Kalman filtering and two adaptive techniques employing least squares with forgetting factor and the projection algorithm, respectively, are evaluated in terms of their prediction error.