Advanced Estimation

10 Units

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.

Faculty Faculty of Engineering and Built Environment
School School of Engineering
Availability Semester 1 - 2018 (Callaghan)
Learning Outcomes

On successful completion of this 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
Assumed Knowledge ENGG6440 Linear Control and Estimation, ENGG6441 Non-Linear Control and Estimation plus MCHA4000 Mechatronics Design 2 or equivalent
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

Timetable 2018 Course Timetables for MCHA6100
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