Combining Cameras, Magnetometers and Machine-Learning into a Close-Range Localization System for Docking and Homing

Marc Hildebrandt, Leif Christensen, Frank Kirchner

In: MTS/IEEE Oceans 2017 Anchorage. OCEANS MTS/IEEE Conference (OCEANS-2017) September 18-21 Anchorage Alaska United States IEEE 9/2017.


In this work we are describing a multi-modal short- range navigation system for precision positioning tasks such as docking of a robotic vehicle in 3d space. The two input modalities, a monocular camera tracking a visual marker and an array of 3-axis magnetometers tracking a magnet, were chosen to cover a wide range of environmental conditions in order to increase the versatility and robustness of the navigation system. A comprehensive analysis of the individual components of the system is provided, resulting in a reliable estimate of its real- world applicability.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence