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Autonomous aerial cinematography has the potential to enable automaticcapture of aesthetically pleasing videos without requiring human intervention,empowering individuals with the capability of high-end film studios. Currentapproaches either only handle off-line trajectory generation, or offerstrategies that reason over short time horizons and simplistic representationsfor obstacles, which result in jerky movement and low real-life applicability.In this work we develop a method for aerial filming that is able to trade offshot smoothness, occlusion, and cinematography guidelines in a principledmanner, even under noisy actor predictions. We present a novel algorithm forreal-time covariant gradient descent that we use to efficiently find thedesired trajectories by optimizing a set of cost functions. Experimentalresults show that our approach creates attractive shots, avoiding obstacles andocclusion 65 times over 1.25 hours of flight time, re-planning at 5 Hz with a10 s time horizon. We robustly film human actors, cars and bicycles performingdifferent motion among obstacles, using various shot types.

Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming