Data Fusion for Intelligent Systems

The participants learn to independently explore and understand a given topic and present it to the other participants in a concise and coherent way.

Intended Participants	Bachelor Students
Instructors	Marko Ristic, Benjamin Noack
SWS	2
Credits	3
Languages	English
Prerequisites	Basic knowledge of probability and statistics Understanding of linear algebra (matrix and vector operations) Basic understanding of algorithms and data structures
Kick-Off	Friday, 15.10.2021, 15:15 - 16:45 Room 027, Building 28

Course Description

The processing of sensor-produced data is an important task in robotics. Mobile robots, autonomous vehicles, assistance programs and many more systems all rely on an accurate estimate of their current state to make safe and logical decisions. State estimation algorithms therefore play an important role when the processing or fusing of measurements and estimates is required.

This seminar covers the concepts required for the understanding and application of state estimation algorithms and will see students establish a common understanding of the subject as well as an understanding of an individually assigned topic. Examples of covered topics include Kalman filtering, Gaussian noise assumptions, conservative estimate fusion and the modelling of real-life systems.

Registration

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