Module1 - final - A1 - Sensor fusion with GPS and IMU

The assignment is described in this document

Localisation2018.pdf Download Localisation2018.pdf

It is easiest solved in Matlab, where these files Download these files are available for download, however you can also use Octave, Python, Julia, or any other tool of your choice, but you will then need to convert the provided code yourself.

The problem describes how to use sensor fusion by a Kalman filter to do positioning by combining sensor information from a GPS and an IMU (accelerometer and gyro). You will use prerecorded real world data and study the performance in a situation with GPS outage. You will get some experience of tuning a sensor fusion filter in a real situation. You will also study the performance improvement achievable by using an improved motion model and an additional speedometer sensor.

Please see these instructions Links to an external site. for information about what to submit and about the peer-review.

Extra: To really understand the dynamical model and transformations used in the assignment you might want to have a look at

• how quaternions or rotational matrices are used to describe rotations Links to an external site.
• another nice video with further explanation Links to an external site.
• or alternatively this open-gl tutorial about rotations Links to an external site.
• An experiment showing why you might prefer quaternions Links to an external site. (video 2min)

but you will be able to do the exercise without looking at this.

Upload a presentation with text and figures illustrating your solutions before the deadline. Also upload your code.  The solution will be peer reviewed by your colleagues.