Doctor of Philosophy, The Ohio State University, 2023, Civil Engineering

Global Navigation Satellite System (GNSS) positioning performance degrades greatly when satellite signals are blocked or reflected multiple times before reaching the receiver, especially under forest canopies and in urban canyons. In these challenging environments, the accuracy of GNSS range measurements can only attain to tens meters or even worse. The integration of GNSS and Inertial Measurement Unit (IMU) has become a standard practice and core component of various navigation systems to operate under short GNSS gaps. Nevertheless, it cannot provide continuously accurate navigation solution without a high end IMU on board during prolonged GNSS outages. This dissertation investigates alternative navigation methods in GNSS challenged environments. Research is mainly focused on three aspects, (1) integration of laser scanners to improve the navigation accuracy of a single platform, (2) collaborative navigation to improve the navigation accuracy of the whole network of platforms, and (3) multiple outlier detection to ensure the integrity of collaborative navigation. Robust and efficient algorithms were developed to register laser point clouds to support high precision positioning. The algorithms include center determination of spherical targets, which are designed to be deployed in the area and used as anchors, and sphere center matching. The sphere center determination algorithms include laser point indexing, sphere point classification, least-squares fitting, and sphere center refinement. The sphere center matching algorithm exploits the topology of the sphere centers to determine the correspondence between sphere centers. The developed algorithms were tested to be more than four times faster than an implementation of unoptimized Iterative Closest Point (ICP) algorithm on point cloud registration of two real data sets. A framework was established to integrate sensors deployed on different platforms, called as nodes, and internodal range measurements t (open full item for complete abstract)

Committee: Charles Toth (Advisor); Lei Wang (Committee Member); Alper Yilmaz (Committee Member) Subjects: Civil Engineering; Computer Engineering; Electrical Engineering; Engineering; Information Systems; Technology

Master of Science (M.S.), University of Dayton, 2013, Electrical Engineering

We present a 3D reconstruction algorithm designed to support various automation and navigation applications. The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point cloud model of its unknown surroundings. In this thesis, we present the step by step methodology of the development of a reconstruction technique. The original reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation analysis. In an improved version of the algorithm, we utilized optical flow features to create an extremely dense representation model. Although dense, this model is hindered due to its low disparity resolution. As feature points were matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth computations within a scene. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear super resolution. With this addition, the accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of features is directly proportional to the resolution and high frequencies of the input image. Our final contribution of additional preprocessing steps is designed to filter noise points and mismatched features, giving birth to the complete Dense Point-cloud Representation (DPR) technique. We measure the success of DPR by evaluating the visual appea (open full item for complete abstract)

Committee: Asari Vijayan PhD (Committee Chair); Raul Ordonez PhD (Committee Member); Eric Balster PhD (Committee Member) Subjects: Electrical Engineering; Engineering