The University of Newcastle, Australia
Available in 2020

Course handbook


This course builds on the skills in the areas of uncertainty and estimation. Students will deepen their knowledge of Bayesian estimation methods with applications to real-world engineering problems. The course will cover data fusion techniques, including the topic of data association, advanced system identification, filtering versus smoothing, sensor modelling and calibration, robot mapping and map types, robot localisation, and simultaneous localisation and mapping.

Availability2020 Course Timetables


  • Semester 1 - 2020

Learning outcomes

On successful completion of the course students will be able to:

1. Formulate and solve robot localisation problems.

2. Formulate and solve robot mapping problems.

3. Formulate and solve simultaneous robot localisation and mapping.

4. Apply Bayesian estimation to advanced system identification problems including sensor calibration.

5. Discern between prediction, filtering and smoothing problems and under what circumstances each should be used.


This course will cover:

  • Review of Bayesian Estimation
  • Data fusion
  • Data association
  • Advanced system identification
  • Prediction, filtering and smoothing for general state-space systems
  • Sensor modelling and calibration
  • Robot mapping problem and map types
  • Robot localisation
  • Simultaneous localisation and mapping


This course is restricted to students in the Master of Professional Engineering (Mechatronics) 40063 program and those who meet the assumed knowledge. Students who are not enrolled in the MPE (Mechatronics) 40063 program, but can demonstrate the assumed knowledge, will only be able to enrol after discussion with the course co-ordinator.

Assumed knowledge

MCHA3500 Mechatronics Design 1 or MCHA6500 Mechatronics Design 1

Assessment items

Written Assignment: Assignment 1

Written Assignment: Assignment 2

Written Assignment: Assignment 3

Contact hours



Face to Face On Campus 4 hour(s) per Week for Full Term


Face to Face On Campus 4 hour(s) per Week for Full Term