Indoor Navigation Strategies for Aerial Autonomous Systems presents the necessary and sufficient theoretical basis for those interested in working in unmanned aerial vehicles, providing three different approaches to mathematically represent the dynamics of an aerial vehicle. The book contains detailed information on fusion inertial measurements for orientation stabilization and its validation in flight tests, also proposing substantial theoretical and practical validation for improving the dropped or noised signals. In addition, the book contains different strategies to control and navigate aerial systems. The comprehensive information will be of interest to both researchers and practitioners working in automatic control, mechatronics, robotics, and UAVs, helping them improve research and motivating them to build a test-bed for future projects. Provides substantial information on nonlinear control approaches and their validation in flight tests Details in observer-delay schemes that can be applied in real-time Teaches how an IMU is built and how they can improve the performance of their system when applying observers or predictors Improves prototypes with tactics for proposed nonlinear schemes
The ability for vehicles to navigate unknown environments is critical for autonomous operation. Mapping of a vehicle's environment and self-localization within that environment are especially difficult for an Unmanned Aerial Vehicle (UAV) due to the complexity of UAV attitude and motion dynamics, as well as interference from external influences such as wind. By using a stable vehicle platform and taking advantage of the geometric structure typical of most indoor environments, the complexity of the localization and mapping problem can be reduced. Interior wall and obstacle location can be measured using low-cost range sensors. Relative vehicle location within the mapped environment can then be determined. By alternating between mapping and localization, a vehicle can explore its environment autonomously. This paper examines available low-cost range sensors for suitability in solving the mapping and localization problem. A control system and navigation algorithm are developed to perform mapping of indoor environments and localization. Simulation and experimental results are provided to determine feasibility of the proposed approach to indoor navigation.
Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%.
In this book, a new approach to the Industry 4.0 revolution is given. New policies and challenges appear and education in robotics also needs to be adapted to this new era. Together with new factory conceptualization, novel applications introduce new paradigms and new solutions to old problems. The factory opens its walls and outdoor applications are solved with new robot morphologies and new sensors that were unthinkable before Industry 4.0 era. This book presents nine chapters that propose a new outlook for an unstoppable revolution in industrial robotics, from drones to software robots
This book constitutes the thoroughly refereed proceedings of the 9th International Congress on Telematics and Computing, WITCOM 2020, held in Puerto Vallarta, Mexico, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 28 full papers and 3 short papers in this volume were carefully reviewed and selected from 79 submissions. The papers are focused on the topics of deep and machine learning, cybersecurity, wireless networks, computer vision, communications, and education applied to different sceneries of study and COVID-19.
This open access, interdisciplinary book presents innovative strategies in the use of civil drones in the cultural and creative industry. Specially aimed at small and medium-sized enterprises (SMEs), the book offers valuable insights from the fields of marketing, engineering, arts and management. With contributions from experts representing varied interests throughout the creative industry, including academic researchers, software developers and engineers, it analyzes the needs of the creative industry when using civil drones both outdoors and indoors. The book also provides timely recommendations to the industry, as well as guidance for academics and policymakers.
The navigation task for unmanned aerial vehicles (UAVs), such as quadrotors, in an indoor environment becomes challenging as the global positioning system (GPS) and the magnetometer may provide inaccurate aiding measurements and the signals may get jammed. The navigation system design in this thesis integrates a visual navigation block with a inertial navigation system block, which adds information about aiding measurements information for indoor navigation design. The direct visual measurements are feature coordinates that are obtained from images taken from an onboard monocular camera with different positions in the 3D world space. The scaled relative pose measurements are generated through vision algorithm implementations presented in this thesis. The vehicle states are estimated using the extended Kalman filter (EKF) with inputs from a gyroscope and accelerometer. The EKF sensor fusion process combines inertial measurements and the visual aid- ing measurement to get an optimal estimation. This thesis provides two design results: one navigation system assumes that the 3D world feature coordinates are known and that the navigation system is map-based for the feature ex- traction. The other navigation system does not require prior knowledge of the feature location and captures the feature based on map-less vision algorithms with geometry constraints.
Unmanned Aerial Vehicles are often used for reconnaissance, search and rescue, damage assessment, exploration, and other tasks that are dangerous or prohibitively difficult for humans to perform. Often, these tasks include traversing indoor environments where radio links are unreliable, hindering the use of remote pilot links or ground-based control, and effectively eliminating Global Positioning System (GPS) signals as a potential localization method. As a result, any vehicle capable of indoor flight must be able to stabilize itself and perform all guidance, navigation, and control tasks without dependence on a radio link, which may be available only intermittently. Since the availability of GPS signals in unknown environments is not assured, other sensors must be used to provide position information relative to the environment. This research covers a description of different ranging sensors and methods for incorporating them into the overall guidance, navigation, and control system of a flying vehicle. Various sensors are analyzed to determine their performance characteristics and suitability for indoor navigation, including sonar, infrared range sensors, and a scanning laser rangefinder. Each type of range sensor tested has its own unique characteristics and contributes in a slightly different way to effectively eliminate the dependence on GPS.
Drones in Smart-Cities: Security and Performance is the first book dedicated to drones in smart cities, helping address the many research challenges in bringing UAVs into practice. The book incorporates insights from the latest research in Internet of Things, big data, and cloud computing, 5G, and other communication technologies. It examines the design and implementation of UAV, focusing on data delivery, performability, and security. Intended for researchers, engineers, and practitioners, Drones in Smart-Cities: Security and Performance combines the technical aspects with academic theory to help implement the smart city vision around the globe. Addresses UAV and IoT for smart cities applications Examines topics as UAV safety, challenges, localization methods. QoS, simulation tools, and more Collect the relevant knowledge in one resource, saving research time and effort
