General
Welcome to the course Basics of Sensor Fusion.
Lectures
Main lecturer Prof. Simo Särkkä (simo.sarkka@aalto.fi), Office F305, Rakentajanaukio 2
Secondary lecturer Dr. Muhammad Emzir (muhammad.emzir@aalto.fi), Office F307, Rakentajanaukio 2
Office hours: Please send an email to book an appointment.
Exercises and Project work
Dr. Muhammad Emzir (muhammad.emzir@aalto.fi), Office F307, Rakentajanaukio 2
Office hours: Please send an email to book an appointment.
Intended Learning Outcomes
After successfully completing this course, the participants are able to:
- explain the principles and components of sensor fusion systems,
- identify and explain the differences between linear and nonlinear models and their implications on sensor fusion,
- construct models of multi-sensor systems and use least-squares algorithms for sensor fusion
- construct continuous- and discrete-time state-space models based on ordinary differential equations, difference equations, and physical sensor models,
- develop and compare state-space models and Kalman as well as particle filtering algorithms for solving sensor fusion problems.
Assessment Methods and Criteria
Achievement of the intended learning outcomes is assessed through an individual written exam as well as a group project work.
To pass the course, you need to:
- pass the written exam,
- pass the project,
- actively participate in exercises.
Written exam: The written exam is a pen and paper exam. Allowed aids:
- One (1) hand-written A4 paper with notes (written by yourself, i.e., not written w/ computer, not copied from your peers, etc.)
- Pens
- Calculator
- No lecture notes, books, etc.
The grading scale for both the exam and the project is 0-5. The final grade is the average of the written exam and the project.
Study Material
The course is mainly based on lecture notes and handouts that will be made available on the course homepage. Optionally, the students may also purchase the textbook "Statistical Sensor Fusion" by F. Gustafsson (not mandatory).
Prerequisites
Basic knowledge of linear algebra, mathematical statistics, and calculus is required. Knowledge of signals and systems, estimation theory, and electronics may come in handy but is not required.
Schedule
Lectures: Lectures are held on Wednesdays, 14:15 - 16:00 (except for the first lecture on Monday, Sep 9, 2019, 14:15 - 16:00) in F175b, Health Technology House, Otakaari 3.
Preliminary schedule (may be subject to changes):
| Date |
Topic |
Recommended Reading (Lecture Notes) |
|---|---|---|
| 9.9. |
Course Overview and Introduction to Sensor Fusion |
Chapter 1 |
| 11.9. |
Sensors, Models, and Least Squares Criterion |
Chapter 2 |
| 18.9. |
Static Linear Models and Linear Least Squares |
Chapter 3 |
| 25.9. |
Static Nonlinear Models, Gradient Descent, and Gauss-Newton |
Chapter 4, Sections 4.1-4.3 |
| 2.10. |
Gauss-Newton with Line Search and Levenberg-Marquardt Algorithm |
Chapter 4, Sections 4.4-4.8 |
| 9.10. |
Continuous and Discrete Time Dynamic Models | Chapter 5, Sections 5.1-5.2 |
| 16.10. |
Introduction to the Robot Platform |
- |
| 23.10. |
(No lecture, examination week) |
- |
| 30.10. |
Modeling the Robot Platform | - |
| 6.11. |
Discretization of Continuous-Time Dynamic Models |
Chapter 5, Sections 5.3-5.4 |
| 13.11. |
Filtering Problem and Kalman Filtering |
Chapter 6, Sections 6.1-6.2 |
| 20.11. |
Extended and Unscented Kalman Filtering |
Chapter 6, Sections 6.3-6.5 |
| 27.11. |
Bootstrap Particle Filtering |
Chapter 6, Section 6.6 |
| 4.12. |
Course Summary |
Chapters 1-6 |
| 11.12. |
Project Work Q & A |
- |