Checking date: 28/07/2020

Versión en español

Course: 2023/2024

3D Perception
(17848)
Master in Robotics and Automatization (Plan: 296 - Estudio: 77)
EPI


Coordinating teacher: CASTILLO MONTOYA, JOSE CARLOS

Department assigned to the subject: Systems Engineering and Automation Department

Type: Electives
ECTS Credits: 3.0 ECTS

Course: 1º
Semester: 2º



Requirements (Subjects that are assumed to be known)
Programming (C, C++, Python, Matlab, etc.)
Objectives
The main goal of this course is to give the students an overview of the state-of-the-art sensors, techniques and applications for 3D perception related to robotics. The practical component will play a key role, where students will work with 3D point clouds, applying techniques that allow a robot to perceive its surrounding environment.
Skills and learning outcomes
Link to document

Description of contents: programme
1. Introduction - What is 3D perception? - Why is 3D perception useful in robotics? 2. Sensors in 3D perception in robotics - 3D laser scans - Stereo information - Time-of-flight sensors - Sensors based on infrared meshes (Kinect) - Acoustic 3D sensors (3D sonars) 3. Techniques for 3D point clouds processing - Filtering - Segmentation - Recognition - 3D reconstruction (environment mapping) 4. Robotic applications of 3D perception - Smart vehicles - Drones - Robotic arms - Human-Robot Interaction
Learning activities and methodology
Magistral classes, laboratory practical sessions, individual tutorials, and personal work from the students
Assessment System
  • % end-of-term-examination 70
  • % of continuous assessment (assigments, laboratory, practicals...) 30

Basic Bibliography
  • Geoffrey Taylor, Lindsay Kleeman. Visual Perception and Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination. Springer Tracts in Advanced Robotics. 2006
  • Kanatani, Kenichi, Sugaya, Yasuyuki, Kanazawa, Yasush. Guide to 3D Vision Computation. Geometric Analysis and Implementation. Springer . 2016
  • Rudolph Triebel. dimensional Perception for Mobile Robots: Concepts and Approaches for the Acquisition, Efficient Representation, and Semantic Interpretation of Three-dimensional Range Data for Mobile Robots . VDM Verlag. 2008
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Apolloni, Bruno, et al.. Machine learning and robot perception. Springer Science & Business Media. 2005
  • Malik, Aamir Saeed. Depth Map and 3D Imaging Applications: Algorithms and Technologies. IGI Global. 2011
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN


The course syllabus may change due academic events or other reasons.