Onboard Audio and Video Processing for Secure Detection, Localization, and Tracking in Counter-UAV Applications

Nowadays, UAVs are of fundamental importance in numerous civil applications like search and rescue and military applications like monitoring and patrolling or counter-UAV where the remote UAV nodes collect sensor data. In the last case, flying UAVs collect environmental data to be used to contrast external attacks launched by adversary drones. However, due to the limited computing resources on board of the acquisition UAVs, most of the signal processing is still performed on a ground central unit where the sensor data is sent wirelessly. This poses serious security problems from malicious entities such as cyber attacks that exploit vulnerabilities at the application level. One possibility to reduce the risk is to concentrate part of the computing onboard of the remote nodes. In this context, we propose a framework where detection of nearby drones and their localization and tracking can be performed in real-time on the small computing devices mounted on board of the drones. Background subtraction is applied to the video frames for pre-processing with the objective of an on-board UAV detection using machine-vision algorithms. For the localization and tracking of the detected UAV, multi-channel acoustic signals are instead considered and DOA estimations are obtained through the MUSIC algorithm. In this work, the proposed idea is described in detail along with some experiments and, then, methods of effective implementation are provided

Visual-Inertial Odometry on Chip: An Algorithm-and-Hardware Co-design Approach

Autonomous navigation of miniaturized robots (e.g., nano/pico aerial vehicles) is currently a grand challenge for robotics research, due to the need of processing a large amount of sensor data (e.g., camera frames) with limited on-board computational resources. In this paper we focus on the design of a visual-inertial odometry (VIO) system in which the robot estimates its ego-motion (and a landmark-based map) from on- board camera and IMU data. We argue that scaling down VIO to miniaturized platforms (without sacrificing performance) requires a paradigm shift in the design of perception algorithms, and we advocate a co-design approach in which algorithmic and hardware design choices are tightly coupled. Our contribution is four-fold. First, we discuss the VIO co-design problem, in which one tries to attain a desired resource-performance trade-off, by making suitable design choices (in terms of hardware, algorithms, implementation, and parameters). Second, we characterize the design space, by discussing how a relevant set of design choices affects the resource-performance trade-off in VIO. Third, we provide a systematic experiment-driven way to explore the design space, towards a design that meets the desired trade-off. Fourth, we demonstrate the result of the co-design process by providing a VIO implementation on specialized hardware and showing that such implementation has the same accuracy and speed of a desktop implementation, while requiring a fraction of the power.United States. Air Force Office of Scientific Research. Young Investigator Program (FA9550-16-1-0228)National Science Foundation (U.S.) (NSF CAREER 1350685

Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166282/1/itr2bf00873.pd

Autonomous aerial robot for high-speed search and intercept applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Real Time UAV Altitude, Attitude and Motion Estimation form Hybrid Stereovision

International audienceKnowledge of altitude, attitude and motion is essential for an Unmanned Aerial Vehicle during crit- ical maneuvers such as landing and take-off. In this paper we present a hybrid stereoscopic rig composed of a fisheye and a perspective camera for vision-based navigation. In contrast to classical stereoscopic systems based on feature matching, we propose methods which avoid matching between hybrid views. A plane-sweeping approach is proposed for estimating altitude and de- tecting the ground plane. Rotation and translation are then estimated by decoupling: the fisheye camera con- tributes to evaluating attitude, while the perspective camera contributes to estimating the scale of the trans- lation. The motion can be estimated robustly at the scale, thanks to the knowledge of the altitude. We propose a robust, real-time, accurate, exclusively vision-based approach with an embedded C++ implementation. Although this approach removes the need for any non-visual sensors, it can also be coupled with an Inertial Measurement Unit