A robust and highly accurate positioning system is required to transition to fully autonomous vehicles in society.  This thesis investigates the potential for lidar sensors to be a part of a localization system, adding redundancy in case of an outage in a global navigation satellite system GNSS. Point cloud data is recorded on a busy road to experimentally study lidar odometry with dynamic objects present. By matching point clouds with the well-established iterative closest point ICP algorithm, odometry estimates in 6 degrees of freedom are obtained. In this thesis, three ICP variants, point-to-point, point-to-plane and plane-to-plane, are evaluated along with preprocessing and data segmentation techniques to improve accuracy and computational speed.  High-end lidar sensors are known to produce a large amount of data. To achieve real-time performance for the odometry, the point clouds are downsampled using a 3D voxel grid filter to reduce the amount of data by 86% on average. Experiments show that downsampling with a properly tuned voxel grid filter reduces the total process time without sacrificing the accuracy of the estimates. ICP algorithms assume the environment to be static. Therefore dynamic objects can introduce errors in the odometry estimates. Methods to counteract these errors are evaluated. One approach to address this issue, suggested in the literature, is to segment the point cloud into different objects and remove objects smaller than a given threshold. However, experiments on the recorded data set indicate that this method removes too much point cloud data in certain sections, resulting in inaccurate odometry estimates. This problem is especially salient when the environment lacks larger static structures.       However, outlier rejection methods show promising results for suppressing errors caused by dynamic objects. In scan matching, outlier rejection methods can be used to identify and remove individual data point pair associations whose shared distance deviates from the majority in the point clouds. Removing the outliers strengthens the estimates against errors caused by dynamic objects and improves robustness against measurement noise. Experiments in this thesis show that outlier rejection methods can improve translation accuracy with as much as 39% and rotation accuracy with 57% compared to not using any outlier rejection. To improve the accuracy of the estimates, this thesis proposes an approach to divide the lidar point clouds into two subsets, ground points and non-ground points. The scan matching can then be applied to the two subsets separately, enhancing the most relevant information in each subset. Compared to the traditional way of using the entire point clouds in one estimate, experiments show that using the best performing ICP variant, a linearized point-to-plane, in combination with this proposed method improves translation accuracy by 10%, rotation accuracy by 27%, and computational speed by 23%.  The results in this thesis indicate that a lidar odometry solution can be accurate and computationally efficient enough to strengthen a localization system during shorter GNSS outages.

No description available.

With the rapid development of wireless technologies, the demand for positioning services has grown dramatically over the past three decades. The Global Positioning System (GPS) is widely used in wireless devices for positioning purposes. However, in addition to having bulky and expensive equipment, GPS receivers do not operate properly in dense and indoor environments. Difficulties in using GPS lead us to use sensor localization in which the position information is obtained from the measurements collected within the network without the aid of external resources. Sensor localization has been a great topic of interest during past decades. Although many positioning algorithms have been developed previously in the literature, positioning is still a challenging task. There are many factors that can affect the positioning performance if they are neglected or not treated properly. These factors introduce many nuisance parameters which need to be either estimated or considered when the location is estimated. In this work, we exploit cooperative localization as a recent and trending technology and semidefinite programming (SDP) as a powerful tool in our research. Cooperative localization has several advantages over the traditional noncooperative localization in terms of positioning accuracy and localizability. Cooperation is also highly beneficial for networks with few anchor nodes and low communication range. On the other hand, SDP provides an alternative solution to the optimal maximum-likelihood (ML) estimation. Unlike in the ML estimator, convergence to the global minimum is guaranteed in SDP. It also has significantly lower complexity especially for cooperative networks in exchange for small performance degradation. Using these two concepts, four open problems within the area of cooperative localization and tracking in the presence of nuisance parameters are addressed. In particular, we focus on cooperative received signal strength-based localization when the propagation parameters including path-loss exponent and transmit powers are unknown. Cooperative time-of-arrival-based localization in harsh environments in the presence of severe non-line-of-sight (NLOS) propagation is also investigated. Cooperative localization in asynchronous networks is studied where the clock parameters are considered as nuisance parameters and the focus is on a joint synchronization and localization approach. Lastly, source tracking in NLOS environments is studied where source nodes are mobile and their status changes rapidly from LOS to NLOS and vice versa. / Ph. D.

Uncrewed aerial systems (UAS) have steadily become more prevalent in both defense and industrial applications. Nuclear detection and deterrence is one such field that has given rise to many new opportunities for UAS operations. There is a need to research and develop methods to integrate existing radiation detection technology with UAS capable of flying low-altitude missions. This low-altitude scanning can be achieved by combining small and lightweight radiation detectors and state-of-the-art aircraft and avionics. High resolution mapping can then be conducted using the results of these scans. Significant work has been conducted in this field by both private industry and academic institutions, including the Uncrewed Systems Lab (USL) at Virginia Tech. This work seeks to expand this body of knowledge and provide practical experimental information to showcase and validate the efficacy of radiation detection via UAS. Multiple missions were conducted using samples of 137Cs and 60Co as a radioactive source. Various filtering methods were applied to the results of these missions to produce visual maps that aid in the localization of an unknown source to compare various flight parameters. In addition, significant work was conducted to characterize two radiation detectors available to the USL to provide metrics to assist in the UAS design and flight planning. Finally, the detectors were taken to Savannah River National Laboratories to conduct experiments to provide information to aid future designs and missions that wish to detect a wider variety of radioactive sources. / Master of Science / Drones are becoming more common in many applications for both industry and defense. One of these applications is in the field of nuclear detection which seeks to both regulate the shipping of radioactive material as well as aid response to nuclear disasters. Methods need to be researched to combine existing radiation detectors with new drone technology. These new state-of-the-art drones are capable of flying at very low altitudes which can allow for the use of small and lightweight radiation detectors. Past work in this area, including at the Uncrewed Systems Lab (USL) at Virginia Tech, has explored larger scale aircraft as well as simulated radioactive sources. This work expands the existing knowledge of this field by providing scan results from real radioactive sources and drone flights. Multiple search flights were conducted using small quantities of radioactive cesium and cobalt. Maps were then produced using the information from these flights to showcase the system's ability to quickly locate the areas of high radioactivity. Flights were flown with different altitudes and speeds to determine the effects on mapping accuracy. Finally, experiments were conducted at Savannah River National Laboratories on a variety of more controlled nuclear materials to help inform future drone designs and mission planning.

Robots are widely used in domestic and commercial applications. These applications typically involve robots built for a specific the task, thus leading to efficiency in task completion. However, with the increase in completion efficiency comes an increase in the time required for completion of the whole system. Specific tasks create the need for many different robots, all with differing capabilities, causing an increase in development cost and time needed. This raises the issue of whether using an off the shelf system with minor modifications can accurately perform the same tasks. If so, more time can be spent on refining the process leading to completion of the task, resulting in less time spent developing the robot. Consequently, less cost in the life cycle of the system leads to less cost for the end user, thus allowing robots to be used for more applications. This thesis explores using a commercially available robot, Acroname Inc.'s Garcia, to perform mapping and localization tasks. As the robot travels it gathers data about the environment. This data is processed in Matlab and the result of the algorithm is a map. In the creation of the map, mathematical morphology is explored as a means to reduce noise. When the robot has located a corner, Matlab provides the robot with a position estimate correction. This correction allows the robot to better estimate its location resulting in a more accurate map. As the results of this thesis illustrate, with very minor modifications, the robot is capable of accurately performing mapping and localization tasks. The results demonstrate that an off the shelf system is capable of accurately performing tasks for which it was not specifically designed. / Master of Science

Over the past years manufacturing firms have moved their production to low cost regions. Many factors lead to the moving of their production and how these factors influence their decision making process. These have been discussed in various literatures. They also source from low cost regions in order to improve their productivity. Not only do they source from low-cost regions but also to actually produce goods in those countries. Many literatures have talked about the impact on some of the factors but little on the impact of supply base and sourcing parameters. The thesis seek to review the factors that affect production localization decision process and also find out how the supply base and sourcing parameters affects decision making process, whether the supply base and sourcing is really considered and the impact it has in decision making process to localize a new production. Literature review and interviews were used in this process. Four companies namely Haldex AB, ABB, case company A and B were used for the research. The main factors that are been considered for production localization were found out from the companies and practically the impact of the supply base and sourcing parameters during decision making are presented. The results from the research questions and the empirical studies is summarized in a framework that will enable readers to know the main factors that affect production localization, how decision making is done and the main impact of the supply base and sourcing in production localization. In addition some benefits of placing emphasis on the supply base and sourcing parameters.

No description available.

This thesis investigates the development of a low-cost passive acoustic system for localizing moving vessels to monitor areas where human activities such as fishing, snorkeling and poaching are restricted. The system uses several off-the-shelf sensors with unsynchronized clocks where the Time Difference of Arrival (TDOA) or time delay is extracted by cross-correlation of the signal between paired sensors. The cross-correlation function uses phase correlation or Phase Transform (PHAT) which whitens the cross-spectrum in order to de-emphasize dominant frequency components. Using the locations of pairs of sensors as foci, hyperbolic equations can be defined using the time delay between them. With three or more sensors, multiple hyperbolic functions can be calculated which intersect at a unique point: the boat's location. It is also found that increasing separation distances between sensors decreased the correlation between the signals. However larger separation distances have better localization capability than with small distances. Experimental results from the Columbia and Willamette Rivers are presented to demonstrate performance.

LD2668 .T4 1966 Z86 / Master of Science

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / A MIMO radar system for target localization is presented which uses direct-sequence CDMA (DS-CDMA) waveforms. The received DS-CDMA signal at each antenna is expressed directly in terms of the target positions. The waveforms employed are Gold sequences, and hence are not exactly orthogonal. A generalized successive interference cancellation (GSIC) approach is used to resolve multiple scatterers and reduce clutter. Simulation results are presented which suggest the capability to detect weak scatterers in the presence of clutter using the cancellation method.