This paper presents a flexible approach for calibrating omnidirectional single viewpoint sensors from planar grids. Current approaches in the field are either based on theoretical properties and do not take into account important factors such as misalignment or camera-lens distortion or over-parametrised which leads to minimisation problems that are difficult to solve. Recent techniques based on polynomial approximations lead to impractical calibration methods. Our model is based on an exact theoretical projection function to which we add well identified parameters to model real-world errors. This leads to a full methodology from the initialisation of the intrinsic parameters to the general calibration. We also discuss the validity of the approach for fish-eye and spherical models. An implementation of the method is available as opensource software on the author's Web page. We validate the approach with the calibration of parabolic, hyperbolic, wideangle and spherical sensors.
This paper describes a new approach to vision-based control in robotics. The basic idea consists of considering a vision system as a specific sensor dedicated to a task and included in a control servo loop. Once the necessary modeling stage is performed, the framework becomes one of automatic control, and naturally stability and robustness questions arise. The paper is organized as follows: in the introduction, state-of-the-art visual servoing is reviewed. Then the basic concepts for modeling the concerned interactions are given. The interadion smew is thus defined in a general way, and the application to images follows. Starting from the concept of task function, the general framework of the control is then described, and stability results are recalled. The concept of the hybrid task is also presented and then applied to visual sensors. The paper ends with the presentation of several simulation and experimental results, and some guidelines for future work are drawn in the conclusion.
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