On-Line Estimation of Feature Depth for Image-Based Visual Servoing Schemes

In the image-based visual servoing framework, error signals are directly computed from image feature parameters, thus obtaining control schemes which do not need neither a 3-D model of the scene, nor a perfect knowledge of the camera calibration matrix. However, the current value of the depth Z for each considered feature must be known. We propose a method to estimate on-line the value of Z for point features while the camera is moving through the scene, by using tools from nonlinear observer theory. By interpreting Z as a continuous unknown state with known dynamics, we build an estimator which asymptotically recovers the actual depth value for the selected feature.