Robust airborne 3D visual simultaneous localisation and mapping

The aim of this thesis is to present robust solutions to technical problems of airborne three-dimensional (3D) Visual Simultaneous Localisation And Mapping (VSLAM). These solutions are developed based on a stereovision system available onboard Unmanned Aerial Vehicles (UAVs). The proposed airborne VSLAM enables unmanned aerial vehicles to construct a reliable map of an unknown environment and localise themselves within this map without any user intervention. Current research challenges related to Airborne VSLAM include the visual processing through invariant feature detectors/descriptors, efficient mapping of large environments and cooperative navigation and mapping of complex environments. Most of these challenges require scalable representations, robust data association algorithms, consistent estimation techniques, and fusion of different sensor modalities. To deal with these challenges, seven Chapters are presented in this thesis as follows: Chapter 1 introduces UAVs, definitions, current challenges and different applications. Next, in Chapter 2 we present the main sensors used by UAVs during navigation. Chapter 3 presents an important task for autonomous navigation which is UAV localisation. In this chapter, some robust and optimal approaches for data fusion are proposed with performance analysis. After that, UAV map building is presented in Chapter 4. This latter is divided into three parts. In the first part, a new imaging alternative technique is proposed to extract and match a suitable number of invariant features. The second part presents an image mosaicing algorithm followed by a super-resolution approach. In the third part, we propose a new feature detector and descriptor that is fast, robust and detect suitable number of features to solve the VSLAM problem. A complete Airborne Visual Simultaneous Localisation and Mapping (VSLAM) solution based on a stereovision system is presented in Chapter (5). Robust data association filters with consistency and observability analysis are presented in this chapter as well. The proposed algorithm is validated with loop closing detection and map management using experimental data. The airborne VSLAM is extended then to the multiple UAVs case in Chapter (6). This chapter presents two architectures of cooperation: a Centralised and a Decentralised. The former provides optimal precision in terms of UAV positions and constructed map while the latter is more suitable for real time and embedded system applications. Finally, conclusions and future works are presented in Chapter (7).EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Low computational SLAM for an autonomous indoor aerial inspection vehicle

The past decade has seen an increase in the capability of small scale Unmanned Aerial Vehicle (UAV) systems, made possible through technological advancements in battery, computing and sensor miniaturisation technology. This has opened a new and rapidly growing branch of robotic research and has sparked the imagination of industry leading to new UAV based services, from the inspection of power-lines to remote police surveillance. Miniaturisation of UAVs have also made them small enough to be practically flown indoors. For example, the inspection of elevated areas in hazardous or damaged structures where the use of conventional ground-based robots are unsuitable. Sellafield Ltd, a nuclear reprocessing facility in the U.K. has many buildings that require frequent safety inspections. UAV inspections eliminate the current risk to personnel of radiation exposure and other hazards in tall structures where scaffolding or hoists are required. This project focused on the development of a UAV for the novel application of semi-autonomously navigating and inspecting these structures without the need for personnel to enter the building. Development exposed a significant gap in knowledge concerning indoor localisation, specifically Simultaneous Localisation and Mapping (SLAM) for use on-board UAVs. To lower the on-board processing requirements of SLAM, other UAV research groups have employed techniques such as off-board processing, reduced dimensionality or prior knowledge of the structure, techniques not suitable to this application given the unknown nature of the structures and the risk of radio-shadows. In this thesis a novel localisation algorithm, which enables real-time and threedimensional SLAM running solely on-board a computationally constrained UAV in heavily cluttered and unknown environments is proposed. The algorithm, based on the Iterative Closest Point (ICP) method utilising approximate nearest neighbour searches and point-cloud decimation to reduce the processing requirements has successfully been tested in environments similar to that specified by Sellafield Ltd

Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Autonomous search of an airborne release in urban environments using informed tree planning

The use of autonomous vehicles for chemical source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate to the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in a holistic framework. The proposed system intelligently produces potential gas sampling locations based on the current estimation of the wind field and the local map. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations. The proposed informed tree planning algorithm is then tested against the Entrotaxis technique in a series of high fidelity simulations. The proposed system is found to reduce source position error far more efficiently than Entrotaxis in a feature rich environment, whilst also exhibiting vastly more consistent and robust results

NAVIGATION AND AUTONOMOUS CONTROL OF MAVS IN GPS-DENIED ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

Aerial collective systems

Deployment of multiple flying robots has attracted the interest of several research groups in the recent times both because such a feat represents many interesting scientific challenges and because aerial collective systems have a huge potential in terms of applications. By working together, multiple robots can perform a given task quicker or more efficiently than a single system. Furthermore, multiple robots can share computing, sensing and communication payloads thus leading to lighter robots that could be safer than a larger system, easier to transport and even disposable in some cases. Deploying a fleet of unmanned aerial vehicles instead of a single aircraft allows rapid coverage of a relatively larger area or volume. Collaborating airborne agents can help each other by relaying communication or by providing navigation means to their neighbours. Flying in formation provides an effective way of decongesting the airspace. Aerial swarms also have an enormous artistic potential because they allow creating physical 3D structures that can dynamically change their shape over time. However, the challenges to actually build and control aerial swarms are numerous. First of all, a flying platform is often more complicated to engineer than a terrestrial robot because of the inherent weight constraints and the absence of mechanical link with any inertial frame that could provide mechanical stability and state reference. In the first section of this chapter, we therefore review this challenges and provide pointers to state-of-the-art methods to solve them. Then as soon as flying robots need to interact with each other, all sorts of problems arise such as wireless communication from and to rapidly moving objects and relative positioning. The aim of section 3 is therefore to review possible approaches to technically enable coordination among flying systems. Finally, section 4 tackles the challenge of designing individual controllers that enable a coherent behavior at the level of the swarm. This challenge is made even more difficult with flying robots because of their 3D nature and their motion constraints that are often related to the specific architectures of the underlying physical platforms. In this third section is complementary to the rest of this book as it focusses only on methods that have been designed for aerial collective systems

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection