The rise of Unmanned Aerial Vehicles and their increasing use in the cinema industry calls for the creation of dedicated tools. Though there is a range of techniques to automatically control drones for a variety of applications, none have considered the problem of producing cinematographic camera motion in real-time for shooting purposes. In this paper we present our approach to UAV navigation for autonomous cinematography. The contributions of this research are twofold: (i) we adapt virtual camera control techniques to UAV navigation; (ii) we introduce a drone-independent platform for high-level user interactions that integrates cinematographic knowledge. The results presented in this paper demonstrate the capacities of our tool to capture live movie scenes involving one or two moving actors.
Path planning has been challenging the research community for decades and the amount of literature on the matter is significant. In the robotic field especially, a number of approaches have addressed the problem. However, a large amount of this research was dedicated to ground vehicles and therefore did not fully exploit the capacities of UAV. Looking at the research conducted on path planning by the computer graphics community, the specific task of virtual camera control happens to be strongly related to our research topic due to the similar properties of drones and virtual cameras. In @cite_15 , Christie review a large spectrum of the literature on intelligent camera control, mainly consisting of optimization-based or constraint-based approaches. More recently, in @cite_24 , Lino proposed an algebraic solution to the problem of placing a camera given a set of visual properties. This seminal work on static camera placement was later used to propose camera path planning solutions. In Lino2015 and @cite_30 , the authors detail offline solutions to the problem. Closer to our problem, @cite_17 @cite_22 detailed a reactive approach based on steering behaviors.
@cite_30 @cite_22 @cite_24 @cite_15 @cite_17
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In this paper, we present a complete framework for autonomous vehicle navigation using a single camera and natural landmarks. When navigating in an unknown environment for the first time, usual behavior consists of memorizing some key views along the performed path to use these references as checkpoints for future navigation missions. The navigation framework for the wheeled vehicles presented in this paper is based on this assumption. During a human-guided learning step, the vehicle performs paths that are sampled and stored as a set of ordered key images, as acquired by an embedded camera. The visual paths are topologically organized, providing a visual memory of the environment. Given an image of the visual memory as a target, the vehicle navigation mission is defined as a concatenation of visual path subsets called visual routes. When autonomously running, the control guides the vehicle along the reference visual route without explicitly planning any trajectory. The control consists of a vision-based control law that is adapted to the nonholonomic constraint. Our navigation framework has been designed for a generic class of cameras (including conventional, catadioptric, and fisheye cameras). Experiments with an urban electric vehicle navigating in an outdoor environment have been carried out with a fisheye camera along a 750-m-long trajectory. Results validate our approach. For the control of unmanned aerial vehicles UAVs in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit IMU. First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the unknown metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter EKF formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions. This paper presents a novel decision-theoretic approach to control and coordinate multiple active cameras for observing a number of moving targets in a surveillance system. This approach offers the advantages of being able to (a) account for the stochasticity of targets' motion via probabilistic modeling, and (b) address the trade-off between maximizing the expected number of observed targets and the resolution of the observed targets through stochastic optimization. One of the key issues faced by existing approaches in multi-camera surveillance is that of scalability with increasing number of targets. We show how its scalability can be improved by exploiting the problem structure: as proven analytically, our decision-theoretic approach incurs time that is linear in the number of targets to be observed during surveillance. As demonstrated empirically through simulations, our proposed approach can achieve high-quality surveillance of up to 50 targets in real time and its surveillance performance degrades gracefully with increasing number of targets. We also demonstrate our proposed approach with real AXIS 214 PTZ cameras in maximizing the number of Lego robots observed at high resolution over a surveyed rectangular area. The results are promising and clearly show the feasibility of our decision-theoretic approach in controlling and coordinating the active cameras in real surveillance system. The objective of this paper is to develop a vision-based navigation technique for micro aerial vehicles, quadrotor type, to operate in GPS-denied environment. The navigation method has been developed while using appearance-based Visual-Teach-and-Repeat (VT&R) technique. In a teaching phase, a quadrotor is manually navigated along a desired route to collect a set of reference images. In a repeating phase, the quadrotor is able to autonomously follow the desired route using these reference images. Self-localization is developed to determine the current segment of the desired route by a number of Speeded-Up Robust Features (SURF), matched between the current image and the reference images. In this paper, three methods of self-localization are proposed and compared. After performing self-localization, the quadrotor computes appearance-based motion control commands (desired yaw and height) for the next movement in order to keep track of the desired route. This computation is developed on Funnel Lane theory, which was originally proposed in Chen and Birchfield (IEEE Trans. Robot. 25(3), 749---754 (11)) for 2D navigation of a ground vehicle. The paper extends this theory to 3D navigation of a quadrotor.The proposed self-localization methods are tested with several image databases. Finally, an online experiment of proposed VT&R technique is demonstrated using Ar.Drone quadrotor model.