Positionbased monocular visual servoing of an unknown target using online selfsupervised learning Visual servoing, i.e. control with visual information, is a valuable capability in many robotic applications. In particular, position based visual servoing (PBVS) estimates position information from the observed image to generate visual servo control. However, the estimation of the position of an unknown target using monocular images is still difficult due to the complexity of the image information. For the target estimation problem, we propose to integrate three complementary techniques for monocular visual servoing. First, to estimate the probability of a targetxe2x80x99s existence, the learning model with spatial features from convolution neural network is proposed. Second, the extended Kalman filter based on epipolar geometry estimates the 3D position of the target; moreover, from this 3D position, the perception model is trained online by selfgenerated virtual groundtruth. Finally, visual servo control is generated, and the resulting movement helps to construct epipolar geometry. Finally. the experimental validation is performed in a challenging setting involving occlusion and targetxe2x80x99s shape change.

Siamese Convolutional Neural Network for Submillimeteraccurate Camera Pose Estimation and Visual Servoing Visual Servoing (VS), where images taken from a camera typically attached to the robot endeffector are used to guide the robot motions, is an important technique to tackle robotic tasks that require a high level of accuracy. We propose a new neural network, based on a Siamese architecture, for highly accurate camera pose estimation. This, in turn, can be used as a final refinement step following a coarse VS or, if applied in an iterative manner, as a standalone VS on its own. The key feature of our neural network is that it outputs the relative pose between any pair of images, and does so with submillimeter accuracy. We show that our network can reduce pose estimation errors to 0.6 mm in translation and 0.4 degrees in rotation, from initial errors of 10 mm  5 degrees if applied once, or of several cm  tens of degrees if applied iteratively. The network can generalize to similar objects, is robust against changing lighting conditions, and to partial occlusions (when used iteratively). The high accuracy achieved enables tackling lowtolerance assembly tasks downstream: using our network, an industrial robot can achieve 97.5% success rate on a VGAconnector insertion task without any force sensing mechanism.