Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real-time. Existing approaches do not address all aspects of this problem; they either require high-precision motion-capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (a) vision-based target estimation; (b) 3D signed-distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time-horizon camera motion; and (d) learning-based artistic shot selection.We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in-depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU. K E Y W O R D S aerial robotics, cinematography, computer vision, learning, mapping, motion planning Within the filming context, this cost function measures jerkiness of motion, safety, environmental occlusion of the actor, and shot quality (artistic quality of viewpoints). This cost function depends on the environment and , and on the actor forecast ξ a , all of which are sensed on-the-fly. The changing nature of the environment and ξ a demands replanning at a high frequency. Here we briefly touch upon the four components of the cost function J(ξ q ) (refer to Section 7 for details and mathematical expressions): Smoothness J smooth (ξ q ): Penalizes jerky motions that may lead to camera blur and unstable flight; Safety J obs (ξ q , ): Penalizes proximity to obstacles that are unsafe for the UAV; Occlusion J occ (ξ q , ξ a , ): Penalizes occlusion of the actor by obstacles in the environment; Shot quality J shot (ξ q , ξ a , Ω art ): Penalizes poor viewpoint angles and scales that deviate from the desired artistic guidelines, given by the set of parameters Ω art . In its simplest form, we can express J(ξ q ) as a linear composition of each individual cost, weighted by scalars λ i . The objective is to * = ( ) ()={ } J J J J J J xyz J J J J J J x y z J occ + J obs 99.4 ± 2.2 94.2 ± 7.3 86.9 ± 9.3 J obs 98.8 ± 3.0 87.1 ± 8.5 75.3 ± 11.8 Avg. dist. to ξ shot (m) J occ + J obs 0.4 ± 0.4 6.2 ± 11.2 10.7 ± 13.2 J obs 0.05 ± 0.1 0.3 ± 0.2 0.5 ± 0...

The use of drones for aerial cinematography has revolutionized several applications and industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely controlling a drone while filming a moving target usually requires multiple expert human operators; hence the need for an autonomous cinematographer. Current approaches have severe real-life limitations such as requiring fully scripted scenes, high-precision motion-capture systems or GPS tags to localize targets, and prior maps of the environment to avoid obstacles and plan for occlusion.In this work, we overcome such limitations and propose a complete system for aerial cinematography that combines: (1) a vision-based algorithm for target localization; (2) a real-time incremental 3D signed-distance map algorithm for occlusion and safety computation; and (3) a real-time camera motion planner that optimizes smoothness, collisions, occlusions and artistic guidelines. We evaluate robustness and real-time performance in series of field experiments and simulations by tracking dynamic targets moving through unknown, unstructured environments. Finally, we verify that despite removing previous limitations, our system achieves state-of-the-art performance.

This paper presents a method for modeling and then tracking the 2D planar size, location, orientation, and number of individuals of an animal aggregation using Autonomous Underwater Vehicles (AUVs). It is assumed that the AUVs are equipped with sensors that can measure the position states of a subset of individuals from within the aggregation being tracked. A new aggregation model based on provably stable Markov Process Matrices is shown as a viable model for representing aggregations. Then, a multi-stage state estimation architecture based on Particle Filters is presented that can estimate the time-varying model parameters in real-time using sensor measurements obtained by AUVs. To validate the approach, a historical data set is used consisting of >100 shark trajectories from a leopard shark aggregation observed in the La Jolla, CA coast area. The method is generalizable to any stable group movement model constructed using a Markov Matrix. Simulation results show that, when at least 40% of sharks are tagged, the estimated number of sharks in the aggregation has an error of 6%. This error increased to 27% when the system was tested with real data.

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Fig. 1: We present a multi-UAV system for 3D human reconstruction in the wild. Our framework coordinates the motion of multiple aerial cameras to optimally reconstruct the dynamic target's 3D body pose while avoiding obstacles and occlusions. We deploy the system in challenging real-world conditions and target motions such as jogging and playing soccer.