  Classical Image-Based Visual Servoing (IBVS) makes use of geometric image
features like point, straight line and image moments to control a robotic
system. Robust extraction and real-time tracking of these features are crucial
to the performance of the IBVS. Moreover, such features can be unsuitable for
real world applications where it might not be easy to distinguish a target from
the rest of the environment. Alternatively, an approach based on complete
photometric data can avoid the requirement of feature extraction, tracking and
object detection. In this work, we propose one such probabilistic model based
approach which uses entire photometric data for the purpose of visual servoing.
A novel image modelling method has been proposed using Student Mixture Model
(SMM), which is based on Multivariate Student's t-Distribution. Consequently, a
vision-based control law is formulated as a least squares minimisation problem.
Efficacy of the proposed framework is demonstrated for 2D and 3D positioning
tasks showing favourable error convergence and acceptable camera trajectories.
Numerical experiments are also carried out to show robustness to distinct image
scenes and partial occlusion.
