:: Ph.D. in Information Technologies program at the Electronics, Informatics and Automation Department of UdG.
:: Thesis: Catadioptric Stereo based on Structured Light Projection.
:: Achieved skills: project team work, self management, process coordination.

:: M.S. degree in Computer Science approved with excellent mention from the University of Girona.
:: Relevant coursework: Mobile robotics; 3D perception; Scene interpretation and segmentation; Systems integration: from architectures to communications.

:: Final Thesis: Digital Simulation of Multiquadrant Choppers in Neapolis 4.0. (Frequency modulated power control simulation program). The thesis was elaborated while enrolled at the Technical University of Kavala, Greece and it was granted the top grade by both universities.
:: Automatics and Computer Science Engineering, 5-year, fully taught in English.
   Graduated with 84,2% average.

The catadioptric sensors use reflecting surfaces (convex or planar mirrors) coupled to a conventional camera and are usually classified depending on the way they gather the light rays. When all the observed light rays converge into a point, called focus, the sensors are known as Single View Point Catadioptric Cameras (SVP).

The SVP enables distortion-free reconstruction of panoramic images in a familiar form for the human users.
Take a look at a panoramic image and its perspective-like reconstruction.


I am using the two products from Remote Reality : D40 and S80.

Parabolic mirrors form SVP catadioptric devices when used with telecentric optics.
See how a parabolic mirror behaves when placed in front of an orthographic and perspective camera. Note that the perspective camera case doesn't produce a SVP.

Parabolic mirror with orthographic camera.		Parabolic mirror with perspective camera.

Hyperbolic mirrors form SVP catadioptric devices when used with perspective cameras.

Hyperbolic mirror with perspective camera.

Calibration is the process of estimating the parameters of the model. The camera is calibrated at first and the light projector is subsequently calibrated based on information provided by the camera.

Omnidirectional cameras offer a much wider field of view than the perspective ones and alleviate the problems due to occlusions. However, both types of cameras suffer from the lack of depth perception.
A practical method for obtaining depth in computer vision is to project a known structured light pattern on the scene avoiding the problems and costs involved by stereo vision. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to provide 3D information about the scene.
The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. It is also discussed how this sensor can be used in robot navigation applications.