• Svenska

# Course memo Autumn 2022

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

There are 12 Lectures, two Labs, 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 a tutorial 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.

### Literature

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

## Examination and completion

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

### Examination

• 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.

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

The project report is graded B-E.

Passing the Graph SLAM tutorial as well as doing labs on time earn bonus points that can then raise your grade to the range A-E.

### Opportunity to complete the requirements via supplementary examination

Fx

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.

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

## Further information

No information inserted

31 Oct 2022

### Course offering

• ApplE22 Autumn 2022-50088

English

### Offered By

EECS/Intelligent Systems