On the impact of the camera field-of-view to Direct Visual Servoing robot trajectories when using the Photometric Gaussian Mixtures as dense feature

This paper studies the impact of cameras with different fields of view (FoV) on Direct Visual Servoing to control robot motions from pixel intensities. Focusing on the Photometric Gaussian Mixture Visual Servoing that showed great convergence domains, this paper investigates two types of FoV: the seminal perspective case and the novel full om- nidirectional case. Implemented with our open-source generic software framework libPeR for a fair comparison, the Visual Servoing experiments on a 6 degrees-of-freedom robot arm provide an in-depth evaluation of the impact of each FoV on the convergence domain, straightness of the trajectory and time to reach convergence.