Improvements in robust 2D visual servoing
A fundamental step towards broadening the use of real world image-based visual servoing is to deal with the important issues of reliability and robustness. In order to address this issue, a closed loop control law is proposed that simultaneously accomplishes a visual servoing task and is robust to a general class of image processing errors. This is achieved with the application of widely accepted statistical techniques of robust M-estimation. Furthermore, improvement have been added in the weight computation process: memory, initialization. Indeed, when the error between current visual features and desired ones are large, which occurs when large robot displacement are required, M-estimator may not detect outliers. To address this point, the method we propose to initialize the confidence in each feature is based on the LMedS estimators. Experimental results are presented which demonstrate visual servoing tasks which resist severe outlier contamination.