This article presents a novel framework for performing visual inspection around 3D infrastructures, by establishing a team of fully autonomous Micro Aerial Vehicles (MAVs) with robust localization, planning and perception capabilities. The proposed aerial inspection system reaches high level of autonomy on a large scale, while pushing to the boundaries the real life deployment of aerial robotics. In the presented approach, the MAVs deployed for the inspection of the structure rely only on their onboard computer and sensory systems. The developed framework envisions a modular system, combining open research challenges in the fields of localization, path planning and mapping, with an overall capability for a fast on site deployment and a reduced execution time that can repeatably perform the inspection mission according to the operator needs. The architecture of the established system includes: 1) a geometry-based path planner for coverage of complex structures by multiple MAVs, 2) an accurate yet flexible localization component, which provides an accurate pose estimation for the MAVs by utilizing an Ultra Wideband fused inertial estimation scheme, and 3) visual data post-processing scheme for the 3D model building. The performance of the proposed framework has been experimentally demonstrated in multiple realistic outdoor field trials, all focusing on the challenging structure of a wind turbine as the main test case. The successful experimental results, depict the merits of the proposed autonomous navigation system as the enabling technology towards aerial robotic inspectors.
Localization and state estimation are reaching a certain maturity in mobile robotics, often providing both a precise robot pose estimate at a point in time and the corresponding uncertainty. In the bid to increase the robots' autonomy, the community now turns to more advanced tasks, such as navigation and path planning. For a realistic path to be computed, neither the uncertainty of the robot's perception nor the vehicle's dynamics can be ignored. In this work, we propose to specifically exploit the information on uncertainty, while also accounting for the physical laws governing the motion of the vehicle. Making use of rapidly exploring random belief trees, here we evaluate offline multiple path hypotheses in a known map to select a path exhibiting the motion required to estimate the robot's state accurately and, inherently, to avoid motion in modes, where otherwise observable states are not excited. We demonstrate the proposed approach on a micro aerial vehicle performing visual-inertial navigation. Such a system is known to require sufficient excitation to reach full observability. As a result, the proposed methodology plans safe avoidance not only of obstacles, but also areas where localization might fail during real flights compensating for the limitations of the localization methodology available. We show that our planner actively improves the precision of the state estimation by selecting paths that minimize the uncertainty in the estimated states. Furthermore, our experiments illustrate by comparison that a naive planner would fail to reach the goal within bounded uncertainty in most cases. Au sommaire : snapshot of the evolving 'drone' landscape - how the market will unfold (a view to 2050) - unlocking potential value of drones safety (the role of european level support) Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.  This article presents a software architecture for safe and reliable autonomous navigation of aerial robots in GPS-denied areas. The techniques employed within key modules from this architecture are explained in detail, such as a six-dimensional localization approach based on visual odometry and Monte Carlo localization, or a variant of the Lazy Theta* algorithm for motion planning. The aerial robot used to demonstrate this approach has been extensively tested over the past 2 years for localization and state estimation without any external positioning systems, autonomous local obstacle avoidance, and local path planning among other tasks. This article describes the architecture and main algorithms used to achieve these goals to build a robust autonomous system. The Flying Machine Arena is a platform for experiments and demonstrations with fleets of small flying vehicles. It utilizes a distributed, modular architecture linked by robust communication layers. An estimation and control framework along with built-in system protection components enable prototyping of new control systems concepts and implementation of novel demonstrations. More recently, a mobile version has been featured at several eminent public events. We describe the architecture of the Arena from the viewpoint of system robustness and its capability as a dual-purpose research and demonstration platform. In this paper, we propose a resource-efficient system for real-time 3D terrain reconstruction and landing-spot detection for micro aerial vehicles. The system runs on an on-board smartphone processor and requires only the input of a single downlooking camera and an inertial measurement unit. We generate a two-dimensional elevation map that is probabilistic, of fixed size, and robot-centric, thus, always covering the area immediately underneath the robot. The elevation map is continuously updated at a rate of 1 Hz with depth maps that are triangulated from multiple views using recursive Bayesian estimation. To highlight the usefulness of the proposed mapping framework for autonomous navigation of micro aerial vehicles, we successfully demonstrate fully autonomous landing including landing-spot detection in real-world experiments. On the quest of automating the navigation of challenging and promising Robotics platforms such as small Unmanned Aerial Vehicles (UAVs), the community has been increasingly active in developing perception capabilities able to run onboard such platforms in real-time. Despite that vision-based techniques have been at the heart of recent advancements, the realistic employment onboard UAVs is still in its infancy. Inspired by some of the most recent breakthroughs in online dense scene estimation and borrowing fundamental concepts from Computer Vision, in this work we propose a new pipeline for real-time, local scene reconstruction using a single camera for aerial navigation. Aiming for denser scene estimation than traditional feature-based maps with the ability to run onboard a small UAV in real-time, the proposed approach is demonstrated to achieve unprecedented performance producing rich maps of the camera's workspace, timely enough to serve in obstacle avoidance and real-time interaction of a robot with its direct surroundings. Evaluation on benchmarking datasets and on challenging aerial footage captured with a UAV featuring a conventional camera, reveals dramatic speed-ups, as well as denser and more accurate local reconstructions with respect to the state of the art.