EG504N: LOCALISATION AND MAPPING IN THE INDUSTRIAL DOMAIN (2023-2024)

The course is an introduction to the paradigm of probabilistic robotics, applied in the context of mobile robotics. It covers the SLAM problem and its various forms as well as the different variants of recursive Bayesian filtering (e.g., the KF and EKF) and their underlying assumptions. The course begins with a recap of the basic concepts of random variables, probability distributions, conditional independence and Markov assumption. Then, the characteristics of probabilistic motion and sensor models for mobile robots are introduced just as the differences and similarities between Occupancy Grid Maps and Counting. An overview on the characteristics and designs of landmark- and grid-based SLAM solutions is given, including the effects of Rao-Blackwellization. Additionally, the basic python commands to implement Bayes filters and SLAM systems are conveyed.

Course Content

1. Introduction – Nomenclature, history, state of the art, challenges, course logistics
2. Probability Review – Events, axioms of probability, independent events, Bayes Rule, Bayes Filter
3. Probabilistic Modelling – Odometry- and velocity-based motion models, beam- and scan-based sensor models
4. Localisation with Nonparametric Filters – Discrete Bayes Filter, importance sampling, particle filter
5. Localisation with Gaussian Filters – Kalman Filter, extended Kalman Filter
6. Mapping with Known Poses – Occupancy maps, reflection probability maps
7. Landmark-based SLAM – SLAM problem, EKF SLAM, loop closing, Rao-Blackwellization, FastSLAM
8. Grid-based SLAM – Scan matching, FastSLAM, improved proposals, selective resampling

Information on contact teaching time is available from the course guide.

More Information about Week Numbers

There are no assessments for this course.