In this dissertation we present advances in developing an autonomous air vehicle capable of navigating through unknown, indoor environments. The problem imposes stringent limits on the computational power available onboard the vehicle, but the environment necessitates using 3D sensors such as stereo or RGB-D cameras whose data requires significant processing. We address the problem by proposing and developing key elements of a relative navigation scheme that moves as many processing tasks as possible out of the time-critical functions needed to maintain flight. We present in Chapter 2 analysis and results for an improved multirotor helicopter state estimator. The filter generates more accurate estimates by using an improved dynamic model for the vehicle and by properly accounting for the correlations that exist in the uncertainty during state propagation. As a result, the filter can rely more heavily on frequent and easy to process measurements from gyroscopes and accelerometers, making it more robust to error in the processing intensive information received from the exteroceptive sensors. In Chapter 3 we present BERT, a novel approach to map optimization. The goal of map optimization is to produce an accurate global map of the environment by refining the relative pose transformation estimates generated by the real-time navigation system. We develop BERT to jointly optimize the global poses and relative transformations. BERT exploits properties of independence and conditional independence to allow new information to efficiently flow through the network of transformations. We show that BERT achieves the same final solution as a leading iterative optimization algorithm. However, BERT delivers noticeably better intermediate results for the relative transformation estimates. The improved intermediate results, along with more readily available covariance estimates, make BERT especially applicable to our problem where computational resources are limited. We conclude in Chapter 4 with analysis and results that extend BERT beyond the simple example of Chapter 3. We identify important structure in the network of transformations and address challenges arising in more general map optimization problems. We demonstrate results from several variations of the algorithm and conclude the dissertation with a roadmap for future work.

The design of a low cost electronically steered array feed (ESAF) is implemented and tested. The ESAF demonstrated satellite tracking capabilities over four degrees. The system was compared to a commercial low-noise block downconverter (LNBF) and was able to receive the signal over a wider angle than the commercial system. Its signal-to-noise ratio (SNR) performance was poor, but a proof of concept for a low cost ESAF used for tracking is demonstrated. Two compact low profile dual circularly polarized (CP) reflector feed antenna designs are also analyzed. One of the designs is a passive antenna dipole array over an electromagnetic band gap (EBG) surface. It demonstrated high isolation between ports for orthogonal polarizations while also achieving quality dual CP performance. Simulations and measurements are shown for this antenna. The other antenna was a microstrip cross antenna. This antenna demonstrated high gain and quality CP but had a large side lobe and low isolation between ports. A global positioning system (GPS) denied multiple input multiple output (MIMO) radar for unmanned aerial vehicles (UAVs) is simulated and tested in a physical optics scattering model. This model is developed and tested by comparing simulated and analytical results. The radar uses channel matrices generated from the MIMO antenna system. The channel matrices are then used to generate correlation matrices. A matrix distance between actively received correlation matrices to stored correlation matrices is used to estimate the position of the UAV. Simulations demonstrate the ability of the radar algorithm to determine its position when flying along a previously mapped path.

<p dir="ltr">The Global Navigation Satellite System (GNSS) is heavily relied on for the navigation of Earth satellites. For satellites in cislunar space and beyond, GNSS is not readily available. As a result, other sources such as NASA's Deep Space Network (DSN) must be relied on for navigation. However, DSN is overburdened and can only support a small number of satellites at a time. Furthermore, communication with external sources can become interrupted or deprived in these environments. Given NASA's current efforts towards cislunar space operations and the expected increase in cislunar satellite traffic, there will be a need for more autonomous navigation options in cislunar space and beyond.</p><p dir="ltr">In this thesis, a navigation system capable of accurate and computationally efficient orbit determination in these communication-deprived environments is proposed and investigated. The emphasis on computational efficiency is in support of cubesats which are constrained in size, cost, and mass; this makes navigation even more challenging when resources such as GNSS signals or ground station tracking become unavailable.</p><p dir="ltr">The proposed navigation system, which is called GRAVNAV in this thesis, involves a two-satellite formation orbiting a planet. The primary satellite hosts an Extended Kalman Filter (EKF) and is capable of measuring the relative position of the secondary satellite; accurate attitude estimates are also available to the primary satellite. The relative position measurements allow the EKF to estimate the absolute position and velocity of both satellites. In this thesis, the proposed navigation system is investigated in the two-body and three-body problems.</p><p dir="ltr">The two-body analysis illuminates the effect of the gravity model error on orbit determination performance. High-fidelity gravity models can be computationally expensive for cubesats; however, celestial bodies such as the Earth and Moon have non-uniform and highly-irregular gravity fields that require complex models to describe the motion of satellites orbiting in their gravity field. Initial results show that when a second-order zonal harmonic gravity model is used, the orbit determination accuracy is poor at low altitudes due to large gravity model errors while high-altitude orbits yield good accuracy due to small gravity model errors. To remedy the poor performance for low-altitude orbits, a Gravity Model Error Compensation (GMEC) technique is proposed and investigated. Along with a special tuning model developed specifically for GRAVNAV, this technique is demonstrated to work well for various geocentric and lunar orbits.</p><p><br></p><p dir="ltr">In addition to the gravity model error, other variables affecting the state estimation accuracy are also explored in the two-body analysis. These variables include the six Keplerian orbital elements, measurement accuracy, intersatellite range, and satellite formation shape. The GRAVNAV analysis shows that a smaller intersatellite range results in increased state estimation error. Despite the intersatellite range bounds, semimajor axis, measurement model, and measurement errors being identical for both orbits, the satellite formation shape also has a strong influence on orbit determination accuracy. Formations that place both satellites in different orbits significantly outperform those that place both satellites in the same orbit.</p><p dir="ltr">The three-body analysis primarily focuses on characterizing the unique behavior of GRAVNAV in Near Rectilinear Halo Orbits (NRHOs). Like the two-body analysis, the effect of the satellite formation shape is also characterized and shown to have a similar impact on the orbit determination performance. Unlike the two-body problem, however, different orbits possess different stability properties which are shown to significantly affect orbit determination performance. The more stable NRHOs yield better GRAVNAV performance and are also less sensitive to factors that negatively impact performance such as measurement error, process noise, and decreased intersatellite range.</p><p dir="ltr">Overall, the analyses in this thesis show that GRAVNAV yields accurate and computationally efficient orbit determination when GMEC is used. This, along with the independence of GRAVNAV from GNSS signals and ground-station tracking, shows that GRAVNAV has good potential for navigation in cislunar space and beyond.</p>

A system of an unmanned aerial vehicle (UAV) collaborating with an unmanned ground vehicle (UGV) for use in for example surveillance, reconnaissance, transport and target acquisition is studied. The project investigates the problem of estimating the relative position, velocity and orientation between the UAV and the UGV required to autonomously land the UAV on the UGV during movement. The use of global navigation satellite system (GNSS) receivers are not considered since they are sensitive to interference and spoofing attacks.  The developed estimation system consists of an extended Kalman filter (EKF) using measurements from several sensors, including: a camera, barometers, inertial measurement units (IMUs) and impulse-radio ultra-wide bandwidth (IRUWB) transceivers. Primarily the use of near infrared (NIR) light emitting diodes (LEDs) attached to the UGV and a camera on the UAV is investigated. Several configurations of LED placements, and what errors to expect when measuring them with the camera, are evaluated. The performance is evaluated in both simulations and hardware sensor tests, but no live experiments that include any autonomous landing manoeuvre are conducted.  The results indicate that high estimation precision can be achieved, at close range the errors in position average below 2 cm and in orientation under 0.5 degrees. However, some problems arise from the detection and identification of the LEDs. Further, if measurements of the LEDs are completely missing, the estimation precision suffers due to error accumulation in the inertial navigation. These results indicate that autonomous landing is possible, since the amount of LED measurements and consequently also the estimation precision increases as the relative position decreases.

Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly with GPS localization and advanced mission planning software. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy in GPS-denied environments. Developments in lidar technology and Simultaneous Localization and Mapping (SLAM) algorithms are enabling UAVs to function safely underground where they can be used to map workings and digitally reconstruct them into 3D point clouds for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drone-based lidar and photogrammetry were used to capture 3D point clouds of underground mines, and the associated applications of mine digitization, such as geotechnical analysis and pillar strength analysis. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites. / Doctor of Philosophy / Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy. New developments are enabling UAVs to function safely underground where they can be used to digitally reconstruct workings for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drones were used to create 3D models of mines, and the associated applications of mine digitization. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites.

This report documents the development of the Subwave, a remotely-operated underwater vehicle (ROV), and an artificial neural network based inertial positioning system. The Subwave uses the open-source ArduSub software framework, commercial-off-the-shelf hardware components, and several custom systems. It is designed as a platform for researching autonomous underwater vehicles (AUVs). The first step for an AUV is navigating waypoints, which requires the AUV to know its global position. Since global navigation satellite systems (GNSSs) are denied underwater, the available underwater positioning systems were surveyed and determined that all the available systems were too large and expensive for the Subwave. It was also discovered that the only consistent underwater positioning method was inertial positioning. So, experimentation began on a small, low-cost system that employs an artificial neural network to predict latitude and longitude using micro-electromechanical system (MEMS) inertial measurement unit (IMU) data as inputs, which would become the Neural-Inertial Positioning System.

Most micro air vehicles rely heavily on reliable GPS measurements for proper estimation and control, and therefore struggle in GPS-degraded environments. When GPS is not available, the global position and heading of the vehicle is unobservable. This dissertation establishes the theoretical and practical advantages of a relative navigation framework for MAV navigation in GPS-degraded environments. This dissertation explores how the consistency, accuracy, and stability of current navigation approaches degrade during prolonged GPS dropout and in the presence of heading uncertainty. Relative navigation (RN) is presented as an alternative approach that maintains observability by working with respect to a local coordinate frame. RN is compared with several current estimation approaches in a simulation environment and in hardware experiments. While still subject to global drift, RN is shown to produce consistent state estimates and stable control. Estimating relative states requires unique modifications to current estimation approaches. This dissertation further provides a tutorial exposition of the relative multiplicative extended Kalman filter, presenting how to properly ensure observable state estimation while maintaining consistency. The filter is derived using both inertial and body-fixed state definitions and dynamics. Finally, this dissertation presents a series of prolonged flight tests, demonstrating the effectiveness of the relative navigation approach for autonomous GPS-degraded MAV navigation in varied, unknown environments. The system is shown to utilize a variety of vision sensors, work indoors and outdoors, run in real-time with onboard processing, and not require special tuning for particular sensors or environments. Despite leveraging off-the-shelf sensors and algorithms, the flight tests demonstrate stable front-end performance with low drift. The flight tests also demonstrate the onboard generation of a globally consistent, metric, and localized map by identifying and incorporating loop-closure constraints and intermittent GPS measurements. With this map, mission objectives are shown to be autonomously completed.

Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Positioning System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles (UAVs) that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments. Therefore, this work investigates methods for incorporating a monocular vision sensor into a standard avionics suite. Vision sensors possess the potential to extract information about the surrounding environment and determine the locations of features or points of interest. Having mapped out landmarks in an unknown environment, subsequent observations by the vision sensor can in turn be used to resolve aircraft position and orientation while continuing to map out new features. An extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is presented. Feature points are detected in each image using a Harris corner detector, and these feature measurements are corresponded from frame to frame using a statistical Z-test. When GPS is available, sequential observations of a single landmark point allow the point's location in inertial space to be estimated. When GPS is not available, landmarks that have been sufficiently triangulated can be used for estimating vehicle position and attitude. Simulation and real-time flight test results for vision-based mapping and navigation are presented to demonstrate feasibility in real-time applications. These methods are then integrated into a practical framework for flight in GPS-denied environments and verified through the autonomous flight of a UAV during a loss-of-GPS scenario. The methodology is also extended to the application of vehicles equipped with stereo vision systems. This framework enables aircraft capable of hovering in place to maintain a bounded pose estimate indefinitely without drift during a GPS outage.

Micro air vehicles have recently gained popularity due to their potential as autonomous systems. Their future impact, however, will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. In response to this need, this dissertation investigates a potential solution for GPS-denied operations called relative navigation. The method utilizes keyframe-to-keyframe odometry estimates and their covariances in a global back end that represents the global state as a pose graph. The back end is able to effectively represent nonlinear uncertainties and incorporate opportunistic global constraints. The GPS-denied research community has, for the most part, neglected to consider fixed-wing aircraft. This dissertation enables fixed-wing aircraft to utilize relative navigation by accounting for their sensing requirements. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter and regularly performs relative resets in coordination with keyframe declarations. In addition to the front-end development, a method is provided to account for front-end velocity bias in the back-end optimization. Finally a method is presented for enabling multiple vehicles to improve navigational accuracy by cooperatively sharing information. Modifications to the relative navigation architecture are presented that enable decentralized, cooperative operations amidst temporary communication dropouts. The proposed framework also includes the ability to incorporate inter-vehicle measurements and utilizes a new concept called the coordinated reset, which is necessary for optimizing the cooperative odometry and improving localization. Each contribution is demonstrated through simulation and/or hardware flight testing. Simulation and Monte-Carlo testing is used to show the expected quality of the results. Hardware flight-test results show the front-end estimator performance, several back-end optimization examples, and cooperative GPS-denied operations.

Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architure that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvments to the state-of-the-art in optimal control of quadrotors by utilizing the error-state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve for the position of multiple agents at specific times in their trajectories. This dissertation instead attempts to solve this problem in a relative context, resulting in a much more robust approach that is able to handle much greater intial error than traditional approaches.