Vision-based trailer pose estimation for articulated vehicles

Articulated Heavy Goods Vehicles (HGVs) are more efficient than conventional rigid lorries, but exhibit reduced low-speed manoeuvrability and high-speed stability. Technologies such as autonomous reversing and path-following trailer steering can mitigate this, but practical limitations of the available sensing technologies restrict their commercialisation potential. This dissertation describes the development of practical vision-based articulation angle and trailer off-tracking sensing for HGVs. Chapter 1 provides a background and literature review, covering important vehicle technologies, existing commercial and experimental sensors for articulation angle and off-tracking measurement, and relevant vision-based technologies. This is followed by an introduction to pertinent computer vision theory and terminology in Chapter 2. Chapter 3 describes the development and simulation-based assessment of an articulation angle sensing concept. It utilises a rear-facing camera mounted behind the truck or tractor, and one of two proposed image processing methods: template-matching and Parallel Tracking and Mapping (PTAM). The PTAM-based method was shown to be the more accurate and versatile method in full-scale vehicle tests. RMS measurement errors of 0.4-1.6$^\circ$ were observed in tests on a tractor semi-trailer (Chapter 4), and 0.8-2.4$^\circ$ in tests on a Nordic combination with two articulation points (Chapter 5). The system requires no truck-trailer communication links or artificial markers, and is compatible with multiple trailer shapes, but was found to have increasing errors at higher articulation angles. Chapter 6 describes the development and simulation-based assessment of a trailer off-tracking sensing concept, which utilises a trailer-mounted stereo camera pair and visual odometry. The concept was evaluated in full-scale tests on a tractor semi-trailer combination in which camera location and stereo baseline were varied, presented in Chapter 7. RMS measurement errors of 0.11-0.13 m were obtained in some tests, but a sensitivity to camera alignment was discovered in others which negatively affected results. A very stiff stereo camera mount with a sub-0.5 m baseline is suggested for future experiments. A summary of the main conclusions, a review of the objectives, and recommendations for future work are given in Chapter 8. Recommendations include further refinement of both sensors, an investigation into lighting sensitivity, and alternative applications of the sensors.This work was supported by a "CSIR South Africa Cambridge Scholarship", funded jointly by the Cambridge Commonwealth, European & International Trust and the Council for Scientific & Industrial Research (CSIR South Africa)

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Indoor navigation systems for unmanned aerial vehicles

Ph.DDOCTOR OF PHILOSOPH

Active Perception for Autonomous Systems : In a Deep Space Navigation Scenario

Autonomous systems typically pursue certain goals for an extended amount of time in a self-sustainable fashion. To this end, they are equipped with a set of sensors and actuators to perceive certain aspects of the world and thereupon manipulate it in accordance with some given goals. This kind of interaction can be thought of as a closed loop in which a perceive-reason-act process takes place. The bi-directional interface between an autonomous system and the outer world is then given by a sequence of imperfect observations of the world and corresponding controls which are as well imperfectly actuated. To be able to reason in such a setting, it is customary for an autonomous system to maintain a probabilistic state estimate. The quality of the estimate -- or its uncertainty -- is, in turn, dependent on the information acquired within the perceive-reason-act loop described above. Hence, this thesis strives to investigate the question of how to actively steer such a process in order to maximize the quality of the state estimate. The question will be approached by introducing different probabilistic state estimation schemes jointly working on a manifold-based encapsuled state representation. On top of the resultant state estimate different active perception approaches are introduced, which determine optimal actions with respect to uncertainty minimization. The informational value of the particular actions is given by the expected impact of measurements on the uncertainty. The latter can be obtained by different direct and indirect measures, which will be introduced and discussed. The active perception schemes for autonomous systems will be investigated with a focus on two specific deep space navigation scenarios deduced from a potential mining mission to the main asteroid belt. In the first scenario, active perception strategies are proposed, which foster the correctional value of the sensor information acquired within a heliocentric navigation approach. Here, the expected impact of measurements is directly estimated, thus omitting counterfactual updates of the state based on hypothetical actions. Numerical evaluations of this scenario show that active perception is beneficial, i.e., the quality of the state estimate is increased. In addition, it is shown that the more uncertain a state estimate is, the more the value of active perception increases. In the second scenario, active autonomous deep space navigation in the vicinity of asteroids is investigated. A trajectory and a map are jointly estimated by a Graph SLAM algorithm based on measurements of a 3D Flash-LiDAR. The active perception strategy seeks to trade-off the exploration of the asteroid against the localization performance. To this end, trajectories are generated as well as evaluated in a novel twofold approach specifically tailored to the scenario. Finally, the position uncertainty can be extracted from the graph structure and subsequently be used to dynamically control the trade-off between localization and exploration. In a numerical evaluation, it is shown that the localization performance of the Graph SLAM approach to navigation in the vicinity of asteroids is generally high. Furthermore, the active perception strategy is able to trade-off between localization performance and the degree of exploration of the asteroid. Finally, when the latter process is dynamically controlled, based on the current localization uncertainty, a joint improvement of localization as well as exploration performance can be achieved. In addition, this thesis comprises an excursion into active sensorimotor object recognition. A sensorimotor feature is derived from biological principles of the human perceptual system. This feature is then employed in different probabilistic classification schemes. Furthermore, it enables the implementation of an active perception strategy, which can be thought of as a feature selection process in a classification scheme. It is shown that those strategies might be driven by top-down factors, i.e., based on previously learned information, or by bottom-up factors, i.e., based on saliency detected in the currently considered data. Evaluations are conducted based on real data acquired by a camera mounted on a robotic arm as well as on datasets. It is shown that the integrated representation of perception and action fosters classification performance and that the application of an active perception strategy accelerates the classification process

Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Guidance, Navigation and Control for UAV Close Formation Flight and Airborne Docking

Unmanned aerial vehicle (UAV) capability is currently limited by the amount of energy that can be stored onboard or the small amount that can be gathered from the environment. This has historically lead to large, expensive vehicles with considerable fuel capacity. Airborne docking, for aerial refueling, is a viable solution that has been proven through decades of implementation with manned aircraft, but had not been successfully tested or demonstrated with UAVs. The prohibitive challenge is the highly accurate and reliable relative positioning performance that is required to dock with a small target, in the air, amidst external disturbances. GNSS-based navigation systems are well suited for reliable absolute positioning, but fall short for accurate relative positioning. Direct, relative sensor measurements are precise, but can be unreliable in dynamic environments. This work proposes an experimentally verified guidance, navigation and control solution that enables a UAV to autonomously rendezvous and dock with a drogue that is being towed by another autonomous UAV. A nonlinear estimation framework uses precise air-to-air visual observations to correct onboard sensor measurements and produce an accurate relative state estimate. The state of the drogue is estimated using known geometric and inertial characteristics and air-to-air observations. Setpoint augmentation algorithms compensate for leader turn dynamics during formation flight, and drogue physical constraints during docking. Vision-aided close formation flight has been demonstrated over extended periods; as close as 4 m; in wind speeds in excess of 25 km/h; and at altitudes as low as 15 m. Docking flight tests achieved numerous airborne connections over multiple flights, including five successful docking manoeuvres in seven minutes of a single flight. To the best of our knowledge, these are the closest formation flights performed outdoors and the first UAV airborne docking