- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Micro Air Vehicles (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 waypoints 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. Show more
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SubjectAUTONOMOUS MOBILE ROBOTS; Aerial robotics; Mapping and localization; Teach and repeat; SLAM; Industrial inspection; Micro aerial vehicle; planning
Organisational unit03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
NotesConference lecture held on May 25, 2018
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