  Industrial facilities often require periodic visual inspections of key
installations. Examining these points of interest is time consuming,
potentially hazardous or require special equipment to reach. MAVs are ideal
platforms to automate this expensive and tedious task. In this work we present
a novel system that enables a human operator to teach a visual inspection task
to an autonomous aerial vehicle by simply demonstrating the task using a
handheld device. To enable robust operation in confined, GPS-denied
environments, the system employs the Google Tango visual-inertial mapping
framework as the only source of pose estimates. In a first step the operator
records the desired inspection path and defines the inspection points. The
mapping framework then computes a feature-based localization map, which is
shared with the robot. After take-off, the robot estimates its pose based on
this map and plans a smooth trajectory through the way points defined by the
operator. Furthermore, the system is able to track the poses of other robots or
the operator, localized in the same map, and follow them in real-time while
keeping a safe distance.
