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EL2320 Applied Estimation 7.5 credits

Course memo Autumn 2024-50237

Version 2 – 03/14/2024, 10:57:14 AM

Course offering

Autumn 2024-50237 (Start date 28 Oct 2024, English)

Language Of Instruction


Offered By

EECS/Intelligent Systems

Course memo Autumn 2024

Course presentation

This course mainly focuses on estimation using Kalman filter and particle filter. In this course you will go into depth and get very familiar with two methods that in the other course you will learn are specific examples of  wider classes of methods.

Course Structure

There are 12 lectures. Both theory and practice of estimation will be covered. Getting practical skills in anything requires you to get hands-on experience and as such the work between the lectures will be very important.

Two labs cover the Kalman and particle filters. 

For the final project, the student should work in pairs and implement an estimation method. Each student needs to write an individual report including a literature study. 

Recommended Prerequisits:

Courses corresponding to SF1624 Algebra and Geometry, SF1901 Probability Theory and Statistics,


The official course book is "Probabilistic robotics" by Thrun, Burgard and Fox, The MIT Press, ISBN 0-262-20162-3. Some of the material will be in the form of research publications. The students are assumed able to research for additional material to solve the project assignment.

Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2022

Content and learning outcomes

Course contents

The course focuses on to give participants practical experience of to use different estimation methods on real problems. Examples that are used in the course been for example from navigation with mobile robots.

The course covers the following: Observability, the Markov assumption, data association, estimation methods such as Kalman Filter, Extended Kalman filters, particle filter, Rao-Blackwellized particle filters, Unscented Kalman filter.

Intended learning outcomes

After passing the course, the student shall be able to

  • describe the parts in recursive Bayesian filtering in terms of probabilities reflect on the relationships between measurement uncertainty, probability theory and estimation methods
  • describe parametric estimation technician and choose and apply appropriate method on problems
  • describe Monte Carlo estimation methods and choose and apply appropriate method on problems

in order to be able to work with estimation.

Learning activities

The course consists of lectures, labs/assignments, a project, and  three  written examinations.  The labs are on the Extended Kalman Filter and the Particle Filter.  They go thruough an example in fine detail.  Lab reports are uploaded and graded pass or fail individually.   

The project  serves two goals, one is to give a deeper understanding of estimation and the other is to give students experience writting a scientific report. 

There is also an assignment on Graph SLAM. 


Preparations before course start

Recommended prerequisites

Courses corresponding to SF1624 Algebra and Geometry, SF1901 Probability Theory and Statistics, SF1635 Signals and Systems, part I. Being able to program in MATLAB.


Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard, and Dieter Fox.

Examination and completion

Grading scale

A, B, C, D, E, FX, F


  • PRO1 - Project, 2.0 credits, Grading scale: P, F
  • PRO2 - Project, 2.0 credits, Grading scale: P, F
  • TEN1 - Examination, 3.5 credits, Grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The final mark is based on how the well student has carried out TEN1, PRO1 and PRO2 in combination.

The section below is not retrieved from the course syllabus:

Project ( PRO1 )  This is based on passing the two labs and one written assignment.

Project ( PRO2 )  This is based on the individual written project report or doing the Graph SLAM tutorial.

Examination ( TEN1 ) Pass Fail Exam.  It is passed by passing all three individual written exams given during the course.

Final grade is based on points earned in the above moments.  Not choosing to do a project will limit your grade to C or less.  

Opportunity to complete the requirements via supplementary examination


To obtain an Fx for the final course grade one must have passed the exams and done all the other assignments  in the course and passed all but one.  The one that was not passed needs to have been close to passing.  Then one can complete that after the course is finished.    

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

The section below is not retrieved from the course syllabus:

Information on how to approach collabortion is given in the first lecture.  Slides are in Canvas.

Further information

No information inserted

Round Facts

Start date

28 Oct 2024

Course offering

  • Autumn 2024-50237

Language Of Instruction


Offered By

EECS/Intelligent Systems


Course Coordinator