GNSS-Free Localization for UAVs in the Wild

Considering the accelerated development of Unmanned Aerial Vehicles (UAVs) applications in both industrial and research scenarios, there is an increasing need for localizing these aerial systems in non-urban environments, using GNSS-Free, vision-based methods. This project studies three different image feature matching techniques and proposes a final implementation of a vision-based localization algorithm that uses deep features to compute geographical coordinates of a UAV flying in the wild. The method is based on matching salient features of RGB photographs captured by the drone camera and sections of a pre-built map consisting of georeferenced open-source satellite images. Experimental results prove that vision-based localization has comparable accuracy with traditional GNSS-based methods, which serve as ground truth

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

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

Improving Navigation in GNSS-challenging Environments: Multi-UAS Cooperation and Generalized Dilution of Precision

This paper presents an approach to tackle navigation challenges for Unmanned Aircraft Systems flying under non nominal GNSS coverage. The concept used to improve navigation performance in these environments consists in using one or more cooperative platforms and relative sensing measurements (based on vision and/or ranging) to the navigation aid. The paper details the cooperative navigation filter which can exploit multiple cooperative platforms and multiple relative measurements, while also using partial GNSS information. The achievable navigation accuracy can be predicted using the concept of "generalized dilution of precision", which derives from applying the idea of dilution of precision to the mathematical structure of the cooperative navigation filter. Values and trends of generalized dilution of precision are discussed as a function of the relative geometry in common GNSS-challenging scenarios. Finally, navigation performance is assessed based on simulations and on multi-drone flight tests

Nonvisible Satellite Estimation Algorithm for Improved UAV Navigation in Mountainous Regions

This paper presents a very simple and computationally efficient algorithm for the calculation of the occlusion points of a scene, observed from a given point of view. This algorithm is used to calculate, in any point of a control volume, the number of visible satellites and the Dilution Of Precision (DOP). Knowledge of these information is extremely important to reject measurements of non-visible satellites and for the reconstruction of a fictitious Digital Elevation Map (DEM), that envelops all the regions characterized by a number of visible satellites lower than a given threshold. This DEM evolves in time according to the platform motion and satellite dynamics. Because of this time dependency, the Digital Morphing Map (DMM) has been defined. When the DMM is available, it can be used by the path planning algorithm to optimise the platform trajectory in order to avoid regions where the number of visible satellites is dramatically reduced, the DOP value is very high and the risk to receive corrupted measurement is large. In this paper also presents the concept of a Safety Bubble Obstacle Avoidance (SBOA) system. This technique takes advantage from the numerical properties of the covariance matrix defined in the Kalman filtering process. A space and time safety bubble is defined according to the DOP value and is used to automatically determine a minimum fly distance from the surrounding obstacles

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure