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

TL;DR

This paper introduces a real-time autonomous drone cinematography method that optimizes smooth, safe, and aesthetically pleasing trajectories by balancing cinematography principles and obstacle avoidance, even with noisy actor data.

Contribution

It presents a novel real-time covariant gradient descent algorithm for trajectory optimization that effectively balances shot quality, safety, and occlusion avoidance in aerial filming.

Findings

Successfully avoided obstacles and occlusion 65 times in 1.25 hours of flight.

Re-plans trajectories at 5 Hz with a 10-second horizon.

Robustly films various moving subjects in complex environments.

Abstract

Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios. Current approaches either only handle off-line trajectory generation, or offer strategies that reason over short time horizons and simplistic representations for 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 off shot smoothness, occlusion, and cinematography guidelines in a principled manner, even under noisy actor predictions. We present a novel algorithm for real-time covariant gradient descent that we use to efficiently find the desired trajectories by optimizing a set of cost functions. Experimental results show that our approach creates attractive shots, avoiding obstacles and occlusion 65 times over 1.25 hours of flight time, re-planning at 5 Hz with a 10 s time horizon. We robustly film human actors, cars and bicycles performing different motion among obstacles, using various shot types.