Indoor Navigation Strategies For Aerial Autonomous Systems

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.

The two first CEAS (Council of European Aerospace Societies) Specialist Conferences on Guidance, Navigation and Control (CEAS EuroGNC) were held in Munich, Germany in 2011 and in Delft, The Netherlands in 2013. ONERA The French Aerospace Lab, ISAE (Institut Supérieur de l’Aéronautique et de l’Espace) and ENAC (Ecole Nationale de l’Aviation Civile) accepted the challenge of jointly organizing the 3rd edition. The conference aims at promoting new advances in aerospace GNC theory and technologies for enhancing safety, survivability, efficiency, performance, autonomy and intelligence of aerospace systems. It represents a unique forum for communication and information exchange between specialists in the fields of GNC systems design and operation, including air traffic management. This book contains the forty best papers and gives an interesting snapshot of the latest advances over the following topics: l Control theory, analysis, and design l Novel navigation, estimation, and tracking methods l Aircraft, spacecraft, missile and UAV guidance, navigation, and control l Flight testing and experimental results l Intelligent control in aerospace applications l Aerospace robotics and unmanned/autonomous systems l Sensor systems for guidance, navigation and control l Guidance, navigation, and control concepts in air traffic control systems For the 3rd CEAS Specialist Conference on Guidance, Navigation and Control the International Program Committee conducted a formal review process. Each paper was reviewed in compliance with standard journal practice by at least two independent and anonymous reviewers. The papers published in this book were selected from the conference proceedings based on the results and recommendations from the reviewers.

Micro aerial vehicles (MAVs) are ideal platforms for surveillance and search and rescue in confined indoor and outdoor environments due to their small size, superior mobility, and hover capability. In such missions, it is essential that the MAV is capable of autonomous flight to minimize operator workload. Despite recent successes in commercialization of GPS-based autonomous MAVs, autonomous navigation in complex and possibly GPS-denied environments gives rise to challenging engineering problems that require an integrated approach to perception, estimation, planning, control, and high level situational awareness. Among these, state estimation is the first and most critical component for autonomous flight, especially because of the inherently fast dynamics of MAVs and the possibly unknown environmental conditions. In this thesis, we present methodologies and system designs, with a focus on state estimation, that enable a light-weight off-the-shelf quadrotor MAV to autonomously navigate complex unknown indoor and outdoor environments using only onboard sensing and computation. We start by developing laser and vision-based state estimation methodologies for indoor autonomous flight. We then investigate fusion from heterogeneous sensors to improve robustness and enable operations in complex indoor and outdoor environments. We further propose estimation algorithms for on-the-fly initialization and online failure recovery. Finally, we present planning, control, and environment coverage strategies for integrated high-level autonomy behaviors. Extensive online experimental results are presented throughout the thesis. We conclude by proposing future research opportunities.

The algorithms presented in this book were designed to achieve an acceptable trade-off between contradictive requirements to the software of small UAV navigation systems: sufficient accuracy and reliability in order to perform required flight missions on the one hand, and acceptable cost and simplicity of this software on the other hand. The core of modern navigation systems is integrated Strapdown Inertial Navigation System (SINS) and GPS, so in this book, the SINS algorithms and the algorithms of sensor fusion are described primarily. Inertial sensors (rate gyros and accelerometers) used in SINS are manufactured on the basis of the MEMS-technology. That is why they possess poor accuracy and need to be corrected with other sensors (GPS, magnetometers, and barometric altimeters). It is necessary to take into account that flight missions of small UAVs are characterized by small flight distances, small flight times, small flight speeds, etc. These properties of small UAV flight missions and properties of MEMS-sensors create a practical background for simplification of the SINS algorithms, simultaneously preserving their accuracy at acceptable levels. The navigation algorithms for gyro-free SINS are also considered. Increasing reliability of the UAV navigation systems requires a solution of the problems of the detection of the faulty sensors. These algorithms are described. Some practical aspects of the operation of navigation systems such as initial alignment, sensors calibration, and laboratory, ground, and flight testing of integrated SINS for small UAVs are also presented. This book will be useful for a wide circle of researchers, engineers, and graduate students involved in modern UAV design and manufacturing.

Master's Thesis from the year 2018 in the subject Engineering - Robotics, National Institute of Technology, Rourkela, language: English, abstract: Indoor navigation is a challenging task due to the absence of Global Positioning System(GPS). This project removes the need for GPS in systems by combining Inertial Navigation Systems (INS) and Visual Navigation Systems (VNS), with the help of machine learning with Artificial and Convolutional Neural Networks.In GPS denied environments a highly accurate INS is necessary, it must also be coupled with another system to bound the continious drift error that is present in INS, for which VNS is employed. The system was implemented using a ground robot to collect ground truth data, which were used as datasets to train a filter that increases the accuracy of the INS. The accuracy of the INS has been proven on the hardware platfrom over multiple datasets. Eventually Visual Navigation data can also be fed into the same system, which for now is implemented in simulation, as an independent system. A software and hardware framework have been developed that can be used in the future for further developments. The project also optimizes visual navigation for use on low power hardware with hardware acceleration for maximized speed. A low cost and scalable indoor navigation system is developed for indoor navigation, which can also be further extended to Autonomous Underwater Vehicles (AUV) in 3D space.

The present document addresses, theoretically and experimentally, the most relevant topics for Unmanned Aerial Vehicles (UAVs) in autonomous and semi-autonomous navigation. According with the multidisciplinary nature of the studied problems, a wide range of techniques and theories are covered in the fields of robotics, automatic control, computer science, computer vision and embedded systems, among others. As part of this thesis, two different experimental platforms were developed in order to explore and evaluate various theories and techniques of interest for autonomous navigation. The first prototype is a quadrotor specially designed for outdoor applications and was fully developed in our lab. The second testbed is composed by a non expensive commercial quadrotor kind AR. Drone, wireless connected to a ground station equipped with the Robot Operating System (ROS), and specially intended to test computer vision algorithms and automatic control strategies in an easy, fast and safe way. In addition, this work provides a study of data fusion techniques looking to enhance the UAVs pose estimation provided by commonly used sensors. Two strategies are evaluated in particular, an Extended Kalman Filter (EKF) and a Particle Filter (PF). Both estimators are adapted for the system under consideration, taking into account noisy measurements of the UAV position, velocity and orientation. Simulations show the performance of the developed algorithms while adding noise from real GPS (Global Positioning System) measurements. Safe and accurate navigation for either autonomous trajectory tracking or haptic teleoperation of quadrotors is presented as well. A second order Sliding Mode (2-SM) control algorithm is used to track trajectories while avoiding frontal collisions in autonomous flight. The time-scale separation of the translational and rotational dynamics allows us to design position controllers by giving desired references in the roll and pitch angles, which is suitable for quadrotors equipped with an internal attitude controller. The 2-SM control allows adding robustness to the closed-loop system. A Lyapunov based analysis probes the system stability. Vision algorithms are employed to estimate the pose of the vehicle using only a monocular SLAM (Simultaneous Localization and Mapping) fused with inertial measurements. Distance to potential obstacles is detected and computed using the sparse depth map from the vision algorithm. For teleoperation tests, a haptic device is employed to feedback information to the pilot about possible collisions, by exerting opposite forces. The proposed strategies are successfully tested in real-time experiments, using a low-cost commercial quadrotor. Also, conception and development of a Micro Aerial Vehicle (MAV) able to safely interact with human users by following them autonomously, is achieved in the present work. Once a face is detected by means of a Haar cascade classifier, it is tracked applying a Kalman Filter (KF), and an estimation of the relative position with respect to the face is obtained at a high rate. A linear Proportional Derivative (PD) controller regulates the UAV's position in order to keep a constant distance to the face, employing as well the extra available information from the embedded UAV's sensors. Several experiments were carried out through different conditions, showing good performance even under disadvantageous scenarios like outdoor flight, being robust against illumination changes, wind perturbations, image noise and the presence of several faces on the same image. Finally, this thesis deals with the problem of implementing a safe and fast transportation system using an UAV kind quadrotor with a cable suspended load. The objective consists in transporting the load from one place to another, in a fast way and with minimum swing in the cable.

Heightened public interest in Unmanned Aerial Systems (UAS) has led recently to a rapid increase in both the number and diversity of small- to medium-sized vehicles in the public airspace. With many of these UAS boasting autonomous capabilities such as hands-free flying and obstacle avoidance, safe and accurate autonomous localization and navigation remains critically important. Various technologies have been developed to solve the problem of accurate localization in an unknown airspace, but highly accurate vision-based navigation solutions continue to see rapid development due to the added challenges posed by indoor navigation. Namely, the lack of a reliable GPS connection in indoor environments proves challenging for precise maneuvering, and many of the highest-fidelity alternatives to GPS-based localization are heavy, expensive, and difficult to implement. Growing consumer and commercial adoption of Virtual and Augmented Reality technologies has led to a sharp increase in the number of compact localization solutions available to the public, and the capabilities of these devices conveniently make them choice candidates in solving the challenges of accurate indoor navigation. In the present study, a UAS navigation solution using the Intel RealSense T265, a commercially available Visual-Inertial Odometry (VIO) device, is developed and presented for the purpose of characterizing indoor localization performance. The goal of the study is to determine whether the localization fidelity of a compact and inexpensive VIO solution is sufficiently high to support safe and reliable autonomy of small indoor aerial vehicles. Position and heading data from the T265 are analyzed in their raw form and also after correction using an Extended Kalman Filter (EKF). These data are gathered by way of a hand-carry test, and are compared to ground truth measurements obtained via a Vicon motion capture system. Additionally, a closed-loop flight test is performed outside of a motion capture room for concept validation purposes and to evaluate the convergence and command tracking capability of the EKF-based navigation system. Results from hand-carry testing examined both the raw data from the T265 and the combined data using the EKF. Localization estimates from the device gathered immediately after initialization are highly inaccurate, but the raw data improves significantly as the VIO device continues to operate and gather information about its environment. The device may indeed prove sufficiently accurate for precision maneuvering applications, but only once it has been running for some time. These findings also suggest that the device may perform well when combined with additional sensors (such as LiDAR) that can "correct" the initial pose estimates and reduce the time required to provide an accurate solution. Further localization improvements may also be achievable with varied software configurations. The performance of the Extended Kalman Filter during the closed-loop flight is also evaluated, and while the EKF does not significantly improve position estimates while the raw device data is still inaccurate, it shows smoothing of noisy T265 measurements and generally precise trajectory following capabilities. Future work to extend this characterization shall involve testing the performance of the device across varying flight envelopes, and especially for longer durations.

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.