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Course description
  PERCEPTION SYSTEM

Competencies and objectives

 

Course context for academic year 2019-20

This course is compulsory for the third year of the degree of Robotics Engineering. This subject deals with concepts related to the perception of a robot. Specifically, everything related to 3D vision (homography, 3D data processing, etc.) and you will see subjects with less depth of touch and other sensory sources such as the voice.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees)

Specific Competences (CE)

  • CE17 : Conocer diferentes clases de dispositivos sensores usados para capturar información del propio robot y de su entorno, así como sus principios de funcionamiento. Saber aplicar los métodos y técnicas para medir, procesar, fusionar y representar la información captada.
  • CE31 : Conocer y entender las técnicas para detección, reconocimiento o seguimiento de elementos dentro del entorno de un robot, y saber utilizar o desarrollar algoritmos para poner en marcha esas técnicas.

 

Transversal Competences

  • CT1 : Capacidades informáticas e informacionales.
  • CT2 : Ser capaz de comunicarse correctamente tanto de forma oral como escrita.
  • CT3 : Capacidad de análisis y síntesis.
  • CT4 : Capacidad de organización y planificación.

 

 

 

Learning outcomes (Training objectives)

No data

 

 

Specific objectives stated by the academic staff for academic year 2019-20

  • To understand the geometric model of the acquisition process of a visual sensor and the basic calibration techniques, and be able to calibrate a camera.
  • To understand the process of correspondence between images of a scene taken from different points of view and know techniques to carry it out.
  • To know the basic transformations in geometry of multiple views (Projective, Aim, Euclidean) based on the movement of the sensor and the projection of perspective and to know how to apply it to processes of estimation of movement and / or 3D reconstruction.
  • To know how to implement methods of visual and tactile perception for the recognition of 3D surfaces.
  • To understand the methodologies of sensorial fusion based on multiple sensors: complementary, cooperative and competitive, and to know sensorial fusion estimators according to the typology of the sensors.

 

 

General

Code: 33730
Lecturer responsible:
MARTINEZ MARTIN, ESTER
Credits ECTS: 6,00
Theoretical credits: 1,20
Practical credits: 1,20
Distance-base hours: 3,60

Departments involved

  • Dept: SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
    Area: SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
    Theoretical credits: 1,2
    Practical credits: 1,2
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught