Combining cameras, magnetometers and machine-learning into a close-range localization system for docking and homing

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.