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

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)

Honeycomb map: a bioinspired topological map for indoor search and rescue unmanned aerial vehicles

The use of robots to map disaster-stricken environments can prevent rescuers from being harmed when exploring an unknown space. In addition, mapping a multi-robot environment can help these teams plan their actions with prior knowledge. The present work proposes the use of multiple unmanned aerial vehicles (UAVs) in the construction of a topological map inspired by the way that bees build their hives. A UAV can map a honeycomb only if it is adjacent to a known one. Different metrics to choose the honeycomb to be explored were applied. At the same time, as UAVs scan honeycomb adjacencies, RGB-D and thermal sensors capture other data types, and then generate a 3D view of the space and images of spaces where there may be fire spots, respectively. Simulations in different environments showed that the choice of metric and variation in the number of UAVs influence the number of performed displacements in the environment, consequently affecting exploration time and energy use.info:eu-repo/semantics/publishedVersio

Cooperative Consensus Simultaneous Localization And Mapping For Multi Blimp System

Navigation in an ocean environment with few static features and dynamic water background is an adventurous field to be explored by multi-agent system. This is because of its non-uniform availability of measurement on the ocean surface since the spatial feature distribution is greatly varied. Thus, it is desirable to design a cooperative localisation and mapping framework that is capable to handle spurious detection, reduce the localisation uncertainty of an agent and achieve fast and good decision. The main objective of this research is to design a cooperative simultaneous localisation and mapping method for multi blimp system involving the dynamic water surface as the background and small flock consensus as the group decision method. A new cooperative framework for the multi blimp system consisting of three blimps and buoys was developed and designed for this purpose. The simultaneous localisation and mapping were designed by integrating three methods which are the Extended Kalman Filter, the enhanced Scale Invariant Feature Transform and Received Signal Strength Indicator to improve the data association process. The group perception of direction based on small flock of animal consensus was taken into the data association process. It was discovered that this cooperative consensus simultaneous localisation and mapping was able to reduce the number of feature points and detect the desired features in clear and dark water environments. In addition, based on cooperative consensus benchmarking, this method was able to achieve faster consensus to up to 8.3 % and 42 % than the scale free model and klemm-eguilez model respectively. On top of these, its heading accuracy was found to be more accurate to up to 30 % and 76 % than the scale free model and klemm-eguilez model respectively. Overall, the proposed approach has achieved its prominent results and it is proven to be significantly reliable and applicable to be implemented in the ocean observation monitoring system

A study on centralised and decentralised swarm robotics architecture for part delivery system

Drones are also known as UAVs are originally designed for military purposes. With the technological advances, they can be seen in most of the aspects of life from filming to logistics. The increased use of drones made it sometimes essential to form a collaboration between them to perform the task efficiently in a defined process. This paper investigates the use of a combined centralised and decentralised architecture for the collaborative operation of drones in a parts delivery scenario to enable and expedite the operation of the factories of the future. The centralised and decentralised approaches were extensively researched, with experimentation being undertaken to determine the appropriateness of each approach for this use-case. Decentralised control was utilised to remove the need for excessive communication during the operation of the drones, resulting in smoother operations. Initial results suggested that the decentralised approach is more appropriate for this use-case. The individual functionalities necessary for the implementation of a decentralised architecture were proven and assessed, determining that a combination of multiple individual functionalities, namely VSLAM, dynamic collision avoidance and object tracking, would give an appropriate solution for use in an industrial setting. A final architecture for the parts delivery system was proposed for future work, using a combined centralised and decentralised approach to combat the limitations inherent in each architecture

A Daisy-Chaining Visual Servoing Approach with Applications in Tracking, Localization, and Mapping

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Small Fixed-wing Aerial Positioning Using Inter-vehicle Ranging Combined with Visual Odometry

There has been increasing interest in developing the ability for small unmanned aerial systems (SUAS) to be able to operate in environments where GPS is not available. This research considers the case of a larger aircraft loitering above a smaller GPS-denied SUAS. This larger aircraft is assumed to have greater resources which can overcome the GPS jamming and provide range information to the SUAS flying a mission below. This research demonstrates that using a ranging update combined with an aircraft motion model and visual odometry can greatly improve the accuracy of a SUASs estimated position in a GPS-denied environment

Vision-Based navigation system for unmanned aerial vehicles

Mención Internacional en el título de doctorThe main objective of this dissertation is to provide Unmanned Aerial Vehicles (UAVs) with a robust navigation system; in order to allow the UAVs to perform complex tasks autonomously and in real-time. The proposed algorithms deal with solving the navigation problem for outdoor as well as indoor environments, mainly based on visual information that is captured by monocular cameras. In addition, this dissertation presents the advantages of using the visual sensors as the main source of data, or complementing other sensors in providing useful information; in order to improve the accuracy and the robustness of the sensing purposes. The dissertation mainly covers several research topics based on computer vision techniques: (I) Pose Estimation, to provide a solution for estimating the 6D pose of the UAV. This algorithm is based on the combination of SIFT detector and FREAK descriptor; which maintains the performance of the feature points matching and decreases the computational time. Thereafter, the pose estimation problem is solved based on the decomposition of the world-to-frame and frame-to-frame homographies. (II) Obstacle Detection and Collision Avoidance, in which, the UAV is able to sense and detect the frontal obstacles that are situated in its path. The detection algorithm mimics the human behaviors for detecting the approaching obstacles; by analyzing the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. Then, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, the algorithm extracts the collision-free zones around the obstacle, and combining with the tracked waypoints, the UAV performs the avoidance maneuver. (III) Navigation Guidance, which generates the waypoints to determine the flight path based on environment and the situated obstacles. Then provide a strategy to follow the path segments and in an efficient way and perform the flight maneuver smoothly. (IV) Visual Servoing, to offer different control solutions (Fuzzy Logic Control (FLC) and PID), based on the obtained visual information; in order to achieve the flight stability as well as to perform the correct maneuver; to avoid the possible collisions and track the waypoints. All the proposed algorithms have been verified with real flights in both indoor and outdoor environments, taking into consideration the visual conditions; such as illumination and textures. The obtained results have been validated against other systems; such as VICON motion capture system, DGPS in the case of pose estimate algorithm. In addition, the proposed algorithms have been compared with several previous works in the state of the art, and are results proves the improvement in the accuracy and the robustness of the proposed algorithms. Finally, this dissertation concludes that the visual sensors have the advantages of lightweight and low consumption and provide reliable information, which is considered as a powerful tool in the navigation systems to increase the autonomy of the UAVs for real-world applications.El objetivo principal de esta tesis es proporcionar Vehiculos Aereos no Tripulados (UAVs) con un sistema de navegacion robusto, para permitir a los UAVs realizar tareas complejas de forma autonoma y en tiempo real. Los algoritmos propuestos tratan de resolver problemas de la navegacion tanto en ambientes interiores como al aire libre basandose principalmente en la informacion visual captada por las camaras monoculares. Ademas, esta tesis doctoral presenta la ventaja de usar sensores visuales bien como fuente principal de datos o complementando a otros sensores en el suministro de informacion util, con el fin de mejorar la precision y la robustez de los procesos de deteccion. La tesis cubre, principalmente, varios temas de investigacion basados en tecnicas de vision por computador: (I) Estimacion de la Posicion y la Orientacion (Pose), para proporcionar una solucion a la estimacion de la posicion y orientacion en 6D del UAV. Este algoritmo se basa en la combinacion del detector SIFT y el descriptor FREAK, que mantiene el desempeno del a funcion de puntos de coincidencia y disminuye el tiempo computacional. De esta manera, se soluciona el problema de la estimacion de la posicion basandose en la descomposicion de las homografias mundo a imagen e imagen a imagen. (II) Deteccion obstaculos y elusion colisiones, donde el UAV es capaz de percibir y detectar los obstaculos frontales que se encuentran en su camino. El algoritmo de deteccion imita comportamientos humanos para detectar los obstaculos que se acercan, mediante el analisis de la magnitud del cambio de los puntos caracteristicos detectados de referencia, combinado con los ratios de expansion de los contornos convexos construidos alrededor de los puntos caracteristicos detectados en frames consecutivos. A continuacion, comparando la proporcion del area del obstaculo y la posicion del UAV, el metodo decide si el obstaculo detectado puede provocar una colision. Por ultimo, el algoritmo extrae las zonas libres de colision alrededor del obstaculo y combinandolo con los puntos de referencia, elUAV realiza la maniobra de evasion. (III) Guiado de navegacion, que genera los puntos de referencia para determinar la trayectoria de vuelo basada en el entorno y en los obstaculos detectados que encuentra. Proporciona una estrategia para seguir los segmentos del trazado de una manera eficiente y realizar la maniobra de vuelo con suavidad. (IV) Guiado por Vision, para ofrecer soluciones de control diferentes (Control de Logica Fuzzy (FLC) y PID), basados en la informacion visual obtenida con el fin de lograr la estabilidad de vuelo, asi como realizar la maniobra correcta para evitar posibles colisiones y seguir los puntos de referencia. Todos los algoritmos propuestos han sido verificados con vuelos reales en ambientes exteriores e interiores, tomando en consideracion condiciones visuales como la iluminacion y las texturas. Los resultados obtenidos han sido validados con otros sistemas: como el sistema de captura de movimiento VICON y DGPS en el caso del algoritmo de estimacion de la posicion y orientacion. Ademas, los algoritmos propuestos han sido comparados con trabajos anteriores recogidos en el estado del arte con resultados que demuestran una mejora de la precision y la robustez de los algoritmos propuestos. Esta tesis doctoral concluye que los sensores visuales tienen las ventajes de tener un peso ligero y un bajo consumo y, proporcionar informacion fiable, lo cual lo hace una poderosa herramienta en los sistemas de navegacion para aumentar la autonomia de los UAVs en aplicaciones del mundo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando García Fernández.- Vocal: Pascual Campoy Cerver

Penerapan Algoritma ORB SLAM-2 Pada Sistem Pemetaan Lingkungan Multi Robot

Sistem multi-robot telah diterapkan pada tugas-tugas kompleks yang biasanya dilakukan oleh manusia. Untuk dapat menjalankan tugasnya, robot perlu bernavigasi ke dari suatu posisi ke posisi lain. Agar dapat bernavigasi dengan baik, robot memerlukan peta sebagai acuannya dalam bernavigasi. Simultaneous Localization and Mapping (SLAM) merupakan sebuah metode bagi robot untuk dapat membuat peta dan melakukan lokalisasi. ORB SLAM-2 merupakan sebuah metode SLAM berbasis sensor visual yang kompatibel terhadap kamera monokular, stereo, maupun RGBD. Dengan menggunakan kamera monokular, penelitian ini bertujuan untuk membuat rancangan sistem pemetaan lingkungan multi-robot dengan menggunakan algoritma ORB SLAM-2. Tugas akhir ini merancang sistem desentralisasi sehingga algoritma dijalankan pada kedua robot. Kemudian setiap robot melakukan pemetaan lingkungannya dan mengirimkannya ke komputer agar dapat divisualisasi. Pada percobaannya, rancangan ini berhasil membuat sistem melaksanakan tugasnya dengan baik. Peta yang dihasilkan oleh sistem ini memiliki skala sekitar 1 : 5,81. Sistem juga dapat memvisualisasikan peta yang dihasilkan oleh masing-masing robot pada sebuah komputer server. Berdasarkan hasil percobaan, dapat disimpulkan bahwa sistem pemetaan lingkungan multi-robot menggunakan ORB SLAM-2 dapat dilakukan dengan mendesentralisasi sistem. Dengan ini, beban kerja sistem terbagi menjadi dua, pertama pemrosesan gambar dilakukan oleh masing-masing robot hingga menghasilkan titik-titik peta dan kedua komputer server bertugas untuk memvisualisasikan titik-titik peta yang dihasilkan robot pada antarmuka pengguna.Sistem multi-robot telah diterapkan pada tugas-tugas kompleks yang biasanya dilakukan oleh manusia. Untuk dapat menjalankan tugasnya, robot perlu bernavigasi ke dari suatu posisi ke posisi lain. Agar dapat bernavigasi dengan baik, robot memerlukan peta sebagai acuannya dalam bernavigasi. Simultaneous Localization and Mapping (SLAM) merupakan sebuah metode bagi robot untuk dapat membuat peta dan melakukan lokalisasi. ORB SLAM-2 merupakan sebuah metode SLAM berbasis sensor visual yang kompatibel terhadap kamera monokular, stereo, maupun RGBD. Dengan menggunakan kamera monokular, penelitian ini bertujuan untuk membuat rancangan sistem pemetaan lingkungan multi-robot dengan menggunakan algoritma ORB SLAM-2. Tugas akhir ini merancang sistem desentralisasi sehingga algoritma dijalankan pada kedua robot. Kemudian setiap robot melakukan pemetaan lingkungannya dan mengirimkannya ke komputer agar dapat divisualisasi. Pada percobaannya, rancangan ini berhasil membuat sistem melaksanakan tugasnya dengan baik. Peta yang dihasilkan oleh sistem ini memiliki skala sekitar 1 : 5,81. Sistem juga dapat memvisualisasikan peta yang dihasilkan oleh masing-masing robot pada sebuah komputer server. Berdasarkan hasil percobaan, dapat disimpulkan bahwa sistem pemetaan lingkungan multi-robot menggunakan ORB SLAM-2 dapat dilakukan dengan mendesentralisasi sistem. Dengan ini, beban kerja sistem terbagi menjadi dua, pertama pemrosesan gambar dilakukan oleh masing-masing robot hingga menghasilkan titik-titik peta dan kedua komputer server bertugas untuk memvisualisasikan titik-titik peta yang dihasilkan robot pada antarmuka pengguna

Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning