COMP0130 Robot Vision and Navigation

This database contains the 2018-19 versions of syllabuses. These are still being finalised and changes may occur before the start of the session.

Syllabuses from the 2017-18 session are available here.

Academic session

2018-19

Module

Robot Vision and Navigation

Code

COMP0130

Module delivery

1819/A7P/T2/COMP0130 Postgraduate

Related deliveries

None

Prior deliveries

COMPGX04

Level

Postgraduate

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 2

Module leader

Julier, Simon

Contributors

Julier, Simon

Agiapito, Lourdes

Groves, Paul

Module administrator

Tickle, Charlie

Aims

Students will gain knowledge about robot real-time pose estimation and mapping, with an emphasis on the use of vision as a primary sensor for mapping the environment. The module will provide students with an understanding and practical experience of how to combine information from satellite navigation and inertial navigation systems, recover geometry from optical sensors and creating an environment map which a robot can use for navigation and motion planning.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. understand the fundamental techniques used for real-time estimation in linear and nonlinear systems
  2. understand how to formulate algorithms to fuse data from satellite and inertial systems to estimate robot position
  3. understand how to formulate mapping and localisation problems in which robots construct sparse maps of their environment
  4. understand how to use camera data to create 3D reconstructions of the environment
  5. programme with Matlab or Python or C++ Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MRes Robotics
  • MSc Business Analytics (with specialisation in Computer Science)
  • MSc Computational Statistics and Machine Learning
  • MSc Computer Graphics, Vision and Imaging
  • MSc Machine Learning
  • MSc Robotics and Computation

Prerequisites:

In order to be eligible to select this module, students must taken in Term 1:

Content

For navigating safely, robots need the ability to localize themselves autonomously using their onboard sensors. Potential tasks include the automatic 3D reconstruction of buildings. inspection and surveillance. This module will teach current techniques for 3D localization, mapping and navigation that are suitable for robotics covering the following topics:

  • Filtering techniques and data fusion
  • Motion estimation and mappig using Simultaneous Localisation and Mapping (SLAM) techniques
  • Non-linear minimization for 3D reconstruction using structure-from-motion

An indicative reading list is available via http://reainglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through a combination of lectures and written and programming exercises.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Coursework 1

33

 

2

Coursework 2

33

 

3

Coursework 3

34

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.