COMPGX04 - Robot Vision and Navigation

This database contains the 2017-18 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Year MSc
Prerequisites COMPGX01, COMPGX02 and COMPGX03
Term 2
Taught By Lourdes Agapito, Simon Julier and Paul Groves
Aims Students will gain knowledge about robot navigation with specific focus 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 recovering geometry from optical sensors and creating an environment map which a robot can use for navigation and motion planning.
Learning Outcomes

Students successfully completing the module should understand:

1. Visual odometry and 3D reconstruction

2. Path planning for autonomous motion

3. Navigation within known or unknown environments

Programming with Matlab or Python or C++ and ROS


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 course will teach current techniques for 3D localization, mapping and navigation that are suitable for robotics covering the following topics:
• Motion estimation from images using visual odometry and SLAM
• Filtering techniques and data fusion
• Non-linear minimization for 3D reconstruction using structure-from-motion
• Autonomous navigation, path planning, exploration of unknown environments

Method of Instruction

Lectures and lab classes. Simon Julier, Lourdes Agapito and Paul Groves will deliver lecture classes. Labs will use graduate demonstrators.


The course has the following assessment components:

  • Coursework (100%)

To pass this course, students must:

  • Obtain an overall pass mark of 50% for all sections combined.


Reading list available via the UCL Library catalogue.