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Probabilistic Localisation and Mapping, 4 credits

Course information

Research Education Subject

  • Computer Science

Course Syllabus

Course Syllabus

Contacts

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