cam a22 4500 55528557X 20201202094757.0 cr unu---uuuuu 190206s2018 xx sm 000 0 eng 10.3929/ethz-b-000322768 doi (ETHRESEARCH)oai:www.research-collection.ethz.ch:20.500.11850/322768 Hepp Benjamin Planning for Autonomous Micro-Aerial Vehicles with Applications to Filming and 3D Modeling [Elektronische Daten] [Benjamin Hepp, Otmar; id_orcid 0000-0002-5068-3474 Hilliges (Supervisor), Marc Pollefeys (Supervisor), Neel Joshi (Supervisor)] Zurich ETH Zurich 2018 190 p. Doctoral Thesis Open access ethresearch Camera equipped micro aerial vehicles (MAVs), and in particular multi-copters, have become affordable and abundant in recent years both in the professional and in the consumer sector. They are used in areas as diverse as recreation, filming, structural inspection and monitoring for agriculture. Their success can be attributed to their relatively simple mechanical and electronic design, their flexibility in terms of possible motion and their smooth dynamics which are easy to handle from a control perspective. In most of these use cases the MAVs are still deployed in a manual fashion where one or more human experts steer the MAV or perform related work. This can be partly attributed to the difficulty of autonomous flight in GPS-denied environments. However, even with a good GPS signal the design of autonomous systems requires the incorporation of domain knowledge from the corresponding use case. This is very challenging as this knowledge of experts is often intuitive and non-formal. In this thesis we demonstrate the design of such systems that take sensory information and provide a sequence of decisions to solve the given task. We will identify and formalize the goals for solving the task and the constraints that need to be fulfilled and incorporate both into a planning scheme that will provide us with a sequence of decisions to achieve the goals. In the first part of the thesis we will focus on a framework that allows us to generate flight trajectories that demonstrate smooth and pleasant motion of the camera. The constraints come in the form of the MAV dynamics and the goals are derived from a keyframe based description of the desired film composition. This leads to a constrained optimization problem over the MAV trajectory that we can solve with non-linear programming. The second part of the thesis introduces a system that provides flight paths to capture an image collection that is suitable for reconstructing high quality 3D models of a user-defined region of interest. Here the major constraints are battery time of the MAV and collision free motion. We formulate this task as a submodular orienteering problem on a graph of possible viewpoints and provide an approximate solver. The third and final part of the thesis looks at the autonomous exploration of unknown environments. Here, the goal is to discover as much surface in as little time as possible. To achieve this goal we define an expert policy with full knowledge of the environment and train a 3D convolutional neural network to imitate this expert policy. The resulting model can then be used to explore an unknown environment. In Copyright - Non-Commercial Use Permitted http://rightsstatements.org/page/InC-NC/1.0 ethresearch Computer Vision ethresearch 3D modeling ethresearch Autonomous systems ethresearch Engineering & allied operations ethresearch Hilliges Otmar; id_orcid 0000-0002-5068-3474 Supervisor dgs Pollefeys Marc Supervisor dgs Joshi Neel Supervisor dgs http://hdl.handle.net/20.500.11850/322768 text/html WWW-Backlink auf das Repository (Open access) BK020353 XK020053 XK020000 1 Doctoral Thesis ethresearch ETHRESEARCH ETHRESEARCH ETHRESEARCH Doctoral Thesis Open access ETHRESEARCH 856 40 http://hdl.handle.net/20.500.11850/322768 text/html WWW-Backlink auf das Repository (Open access) ETHRESEARCH 100 1- Hepp Benjamin ETHRESEARCH 700 1- Hilliges Otmar; id_orcid 0000-0002-5068-3474 Supervisor dgs ETHRESEARCH 700 1- Pollefeys Marc Supervisor dgs ETHRESEARCH 700 1- Joshi Neel Supervisor dgs SWISSBIB 55528557X