A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computationally-efficient visual inertial odometry for autonomous vehicle

This thesis presents the design, implementation, and validation of a novel nonlinearfiltering based Visual Inertial Odometry (VIO) framework for robotic navigation in GPSdenied environments. The system attempts to track the vehicle’s ego-motion at each time instant while capturing the benefits of both the camera information and the Inertial Measurement Unit (IMU). VIO demands considerable computational resources and processing time, and this makes the hardware implementation quite challenging for micro- and nanorobotic systems. In many cases, the VIO process selects a small subset of tracked features to reduce the computational cost. VIO estimation also suffers from the inevitable accumulation of error. This limitation makes the estimation gradually diverge and even fail to track the vehicle trajectory over long-term operation. Deploying optimization for the entire trajectory helps to minimize the accumulative errors, but increases the computational cost significantly. The VIO hardware implementation can utilize a more powerful processor and specialized hardware computing platforms, such as Field Programmable Gate Arrays, Graphics Processing Units and Application-Specific Integrated Circuits, to accelerate the execution. However, the computation still needs to perform identical computational steps with similar complexity. Processing data at a higher frequency increases energy consumption significantly. The development of advanced hardware systems is also expensive and time-consuming. Consequently, the approach of developing an efficient algorithm will be beneficial with or without hardware acceleration. The research described in this thesis proposes multiple solutions to accelerate the visual inertial odometry computation while maintaining a comparative estimation accuracy over long-term operation among state-ofthe- art algorithms. This research has resulted in three significant contributions. First, this research involved the design and validation of a novel nonlinear filtering sensor-fusion algorithm using trifocal tensor geometry and a cubature Kalman filter. The combination has handled the system nonlinearity effectively, while reducing the computational cost and system complexity significantly. Second, this research develops two solutions to address the error accumulation issue. For standalone self-localization projects, the first solution applies a local optimization procedure for the measurement update, which performs multiple corrections on a single measurement to optimize the latest filter state and covariance. For larger navigation projects, the second solution integrates VIO with additional pseudo-ranging measurements between the vehicle and multiple beacons in order to bound the accumulative errors. Third, this research develops a novel parallel-processing VIO algorithm to speed up the execution using a multi-core CPU. This allows the distribution of the filtering computation on each core to process and optimize each feature measurement update independently. The performance of the proposed visual inertial odometry framework is evaluated using publicly-available self-localization datasets, for comparison with some other open-source algorithms. The results illustrate that a proposed VIO framework is able to improve the VIO’s computational efficiency without the installation of specialized hardware computing platforms and advanced software libraries

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Wireless indoor localisation within the 5G internet of radio light

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld and simulation data is used to examine the hybrid millimeter wave and Visible Light Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project. Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous sampling networks are identified, and more accurate and efficient solutions are developed. Currently, VLP relies strongly on the assumed Lambertian properties of light sources. However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC technology in numerous environments, measurements from non-Lambertian sources are analysed to provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP calibration technique is proposed, and results indicate a 59% accuracy improvement against existing methods. This solution enables high accuracy centimetre level VLP to be achieved with non- Lambertian sources. Asynchronous sampling of range-based measurements is known to impact localisation performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to mitigate these effects. While effective at improving positioning performance, the exact suitability of such solutions is not evident due to their additional processes, subsequent complexity, and increased costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the computational demand of existing ASLT and motivate the development of a novel solution. The proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation performance with a significant energy reduction of over 50% when compared to current leading ASLT. The work in this thesis demonstrates both the capability for high performance VLP from non- Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled range measurements.Horizon 202

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

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing