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.
One of the most common application areas that MAVs are employed, is in the filming industry, but there are efforts from other industries such as Mining, Oil, and Energy Providers, to invest in the commercialization of MAVs to perform remote inspection applications. Towards this vision, MAVs are powerful tools that have the profund potential to decrease the risks of human life, decrease the execution time and increase the efficiency of the overall inspection task, especially when compared to conventional methods @cite_4 . Despite the fact that the research in the aerial robotics has reached significant milestones regarding localization @cite_0 , planning @cite_14 and perception @cite_9 @cite_6 , successful real-life demonstrations of autonomous inspection systems have been rarely reported in the literature, with the majority of the applications focusing on impressive laboratory trials under full control environments and in most of the cases under the utilization of expensive motion capturing systems @cite_2 or small scale and well defined outdoor environments @cite_10 @cite_15 .
@cite_14 @cite_4 @cite_9 @cite_6 @cite_0 @cite_2 @cite_15 @cite_10
2089829108 2000146415 1942294243 1547236731
The past decade has seen an increased interest towards research involving Autonomous Micro Aerial Vehicles (MAVs). The predominant reason for this is their agility and ability to perform tasks too difficult or dangerous for their human counterparts and to navigate into places where ground robots cannot reach. Among MAVs, rotary wing aircraft such as quadrotors have the ability to operate in confined spaces, hover at a given point in space and perch1 or land on a flat surface. This makes the quadrotor a very attractive aerial platform giving rise to a myriad of research opportunities. The potential of these aerial platforms is severely limited by the constraints on the flight time due to limited battery capacity. This in turn arises from limits on the payload of these rotorcraft. By automating the battery recharging process, creating autonomous MAVs that can recharge their on-board batteries without any human intervention and by employing a team of such agents, the overall mission time can be greatly increased. This paper describes the development, testing, and implementation of a system of autonomous charging stations for a team of Micro Aerial Vehicles. This system was used to perform fully autonomous long-term multi-agent aerial surveillance experiments with persistent station keeping. The scalability of the algorithm used in the experiments described in this paper was also tested by simulating a persistence surveillance scenario for 10 MAVs and charging stations. Finally, this system was successfully implemented to perform a 9½ hour multi-agent persistent flight test. Preliminary implementation of this charging system in experiments involving construction of cubic structures with quadrotors showed a three-fold increase in effective mission time. 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. Cameras are a natural fit for micro aerial vehicles MAVs due to their low weight, low power consumption, and two-dimensional field of view. However, computationally-intensive algorithms are required to infer the 3D structure of the environment from 2D image data. This requirement is made more difficult with the MAV's limited payload which only allows for one CPU board. Hence, we have to design efficient algorithms for state estimation, mapping, planning, and exploration. We implement a set of algorithms on two different vision-based MAV systems such that these algorithms enable the MAVs to map and explore unknown environments. By using both self-built and off-the-shelf systems, we show that our algorithms can be used on different platforms. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main sensor, we maintain a tiled octree-based 3D occupancy map. The MAV uses this map for local navigation and frontier-based exploration. In addition, we use a wall-following algorithm as an alternative exploration algorithm in open areas where frontier-based exploration under-performs. During the exploration, data is transmitted to the ground station which runs large-scale visual SLAM. We estimate the MAV's state with inertial data from an IMU together with metric velocity measurements from a custom-built optical flow sensor and pose estimates from visual odometry. We verify our approaches with experimental results, which to the best of our knowledge, demonstrate our MAVs to be the first vision-based MAVs to autonomously explore both indoor and outdoor environments. Real-time and reliable localization is a prerequisite for autonomously performing high-level tasks with micro aerial vehicles(MAVs). Nowadays, most existing methods use vision system for 6DoF pose estimation, which can not work in degraded visual environments. This paper presents an onboard 6DoF pose estimation method for an indoor MAV in challenging GPS-denied degraded visual environments by using a RGB-D camera. In our system, depth images are mainly used for odometry estimation and localization. First, a fast and robust relative pose estimation (6DoF Odometry) method is proposed, which uses the range rate constraint equation and photometric error metric to get the frame-to-frame transform. Then, an absolute pose estimation (6DoF Localization) method is proposed to locate the MAV in a given 3D global map by using a particle filter. The whole localization system can run in real-time on an embedded computer with low CPU usage. We demonstrate the effectiveness of our system in extensive real environments on a customized MAV platform. The experimental results show that our localization system can robustly and accurately locate the robot in various practical challenging environments.