We propose PiNcH, a methodology to detect the presence of a drone and its current status leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH does not require either any special hardware or to transmit any signal. Indeed, it is fully passive and it resorts to cheap and general purpose hardware. To evaluate the effectiveness of our solution, we collected real communication measurements from the 3DR SOLO drone, being the most popular open-source hardware, running the widespread ArduCopter open-source firmware, mounted on-board on a wide range of commercial amateur drones. Then, we test our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movement of the drone; and, finally, (iv) estimate a lower bound on the time required to identify a drone with the requested level of assurance. The quality and viability of our solution do prove that network traffic analysis can be successfully adopted for drone identification and pave the way for future research in the area.
An identification mechanism based on the correlation between motion observed from an external camera and acceleration measured on each UAV's accelerometer is proposed by @cite_4 . This solution combines FPV information with accelerometer information to remotely control a subset of swarm drones that are not provided with camera, and therefore it requires the collaboration of one or more drones in the swarm to perform the identification.
@cite_4
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Swarms of Unmanned Aerial Vehicles (drones) could provide great benefit when used for disaster response and indoor search and rescue scenarios. In these harsh environments where GPS availability cannot be ensured, prior work often relies on cameras for control and localization. This creates the challenge of identifying each drone, i.e., finding the association between each physical ID (such as their radio address) and their visual ID (such as an object tracker output). To address this problem, prior work relies on visual cues such as LEDs or colored markers to provide unique information for identification. However, these methods often increase deployment difficulty, are sensitive to environmental changes, not robust to distance and might require hardware changes. In this paper, we present IDrone, a robust physical drone identification system through motion matching and actuation feedback. The intuition is to (1) identify each drone by matching the motion detected through their inertial sensors and from an external camera, and (2) control the drones so they move in unique patterns that allow for fast identification, while minimizing the risk of collision involved in controlling drones with uncertain identification. To validate our approach, we conduct both simulation and real experimentation with autonomous drones for the simplified case of a stationary Spotter (powerful drone equipped with the camera). Overall, our initial results show that our approach offers a great tradeoff between fast identification and small collision probability. In particular, IDrone achieves faster identification time than safety-based baseline actuation (one-at-a-time), and significantly higher survival rate compared to fast, non-safety-based baseline actuation (random motion). 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. Unmanned aerial vehicle (UAV) swarms provide situation awareness in potential life-threatening tasks such as emergency response, search and rescue, etc. However, most of these scenarios take place in GPS-denied environments, where accurately localizing each UAV is challenging. Heterogeneous UAV swarms, in which only a subset of the drones carry cameras, face the additional challenge of identifying each individual UAV in order to avoid sending position updates to the wrong drone, thus crashing. This work presents an identification mechanism based on the correlation between motion observed from an external camera, and the acceleration measured on each UAV’s accelerometer. This paper presents a solution to solve the car detection and counting problem in images acquired by means of unmanned aerial vehicles (UAVs). UAV images are characterized by a very high spatial resolution (order of few centimeters), and consequently by an extremely high level of details which calls for appropriate automatic analysis methods. The proposed method starts with a screening step of asphalted zones in order to restrict the areas where to detect cars and thus to reduce false alarms. Then, it performs a feature extraction process based on scalar invariant feature transform thanks to which a set of keypoints is identified in the considered image and opportunely described. Successively, it discriminates between keypoints assigned to cars and all the others, by means of a support vector machine classifier. The last step of our method is focused on the grouping of the keypoints belonging to the same car in order to get a “one keypoint-one car” relationship. Finally, the number of cars present in the scene is given by the number of final keypoints identified. The experimental results obtained on a real UAV scene characterized by a spatial resolution of 2 cm show that the proposed method exhibits a promising car counting accuracy.