Probabilistic Localisation and Mapping, 4 credits
Course information
Research education subject
- Computer Science
Course Syllabus
Contacts
-
Franziska Klügl, Professor
+46 19 303925 -
Josefin Unander-Scharin, Utbildnings- och forskningsadministratör
+46 19 303909
Course content
The course covers the following contents in the context of probabilistic localisation and mapping:
- Bayes filtering
- Kalman filters
- Particle filters
- Monte Carlo optimisation
- Robot motion and sensor models
- SLAM (simultaneous localisation and mapping)
- Data association