After successful completion of the course, students are able to...

• explain the principles of a Kalman filter
• implement a Kalman filter
• command a robotic total station for the automated tracking of dynamic targets (GEOCOM)
• understand the basics of ROS (Robot Operating System)
• build a geodetic network for solving kinematic problems
• realize a bodyframe of a robot for kinematic problems
• determine lever arms to objects in the body frame
• evaluate the performance of a dynamic state estimation using a ground truth data

Learning the basic principles of the Kalman filter through independent implementation using the example of a robot car.
Specifically, sensor fusion using Kalman filters from odometry and tachymeter data is developed.
For this purpose, dynamic measurement data of test drives of a robot car are recorded by the students.
The students learn to realize a geodetic network for the accomplishment of kinmeatic measurement tasks and modern,
automated, geodesic measuring instruments via a programming interface. The results of the state filtering
using Kalman filtering in post-processing are critically evaluated by the students using ground truth data.

Introduction to relevant aspects of robotics (ROS), and interfaces with engineering geodesy (GEOCOM).
Lecture on the principles of sensor fusion with Kalman filter.
Practical implementation of the measurement setup and execution of the measurements in groups in the laboratory.
Independent implementation of a Kalman filter in Python or Matlab.
Critical evaluation of the results based on ground truth data.
Presentation and defense of the results.

Aktive Mitarbeit während des Semesters und Abschlusspräsentation.