Lidar has the ability to supplant or compliment many current measurement technologies in ocean optics. Lidar measures Inherent Optical Properties over long distances without impacting the orientation and assemblages of particles it measures, unlike many systems today which require pumps and flow cells. As an active sensing technology, it has the benefit of being independent of time of day and weather. Techniques to interpret oceanographic lidar lags behind atmospheric lidar inversion techniques to measure optical properties due to the complexity and variability of the ocean. Unlike in the atmosphere, two unknowns in the lidar equation backscattering at 180o (𝛽𝜋) and attenuation (c) do not necessarily covary. A lidar system developed at the Harbor Branch Oceanographic Institute is used as a test bed to validate a Monte-Carlo model to investigate the inversion of optical properties from lidar signals. Controlled tank experiments and field measurements are used to generate lidar waveforms and provide optical situations to model. The Metron EODES backscatter model is used to model waveforms. A chlorophyll based forward optical model provides a set of 1500 unique optical situations which are modeled to test inversion techniques and lidar geometries. Due to issues with the lidar system and model the goal of validating the model as well as a more mature inversion experiment were not completed. However, the results are valuable to show the complexity and promise of lidar systems. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The use of terrestrial laser scanner in forestry seems to be promising technology for new findings about forest ecosystem together with precise information for forest managers and planners. With new technology comes also new methodology of data acquisition, data processing and presentation of results. In this thesis are proposed methodological aspects of scanning setup if forest with analysis of two main obstacles - terrain and tree stems together with estimation of synergic effect of additional scan and optimal distance of such scan. In the following section software for processing of TLS data from forest environment - 3D Forest - is introduced and briefly described. In the last part original and early attempt of the below ground tree biomass reconstruction and volume estimation using TLS data is presented as a part of coppice forest study.

Speckles are a phenomenon which often appears in situations involving lasers. Their properties can be used as an advantage but in the case of LiDAR applications, they’re purelydestructive. The peaks and troughs of the intensity distribution across the collimatinglens can be seen as variations in signal strength at the detector. The project presented inthis paper examines the properties of these intensity patterns and how their various sizeseffects the sampled signal. This is done by experimental measurements with the use ofa coherent LiDAR accompanied by a simulation to recreate and explain the behavioursof the results obtained in the measurements. The study shows a simulation which exclusively takes speckle dependence into account successfully produce similar results asphysical experiments. The varying of subjective speckle sizes on the detector was foundto have little effect on the sampled signal quality, though the improved averaging of thesmaller speckles caused the signal strength to shift in tranquil manner.

Indiana University-Purdue University Indianapolis (IUPUI) / Advancement in modern technology has brought with it an advent of increased interest in self-driving. The rapid growth in interest has caused a surge in the development of autonomous vehicles which in turn brought with itself a few challenges. To overcome these new challenges, automotive companies are forced to invest heavily in the research and development of autonomous vehicles. To overcome this challenge, simulations are a great tool in any arsenal that’s inclined towards making progress towards a self-driving autonomous future. There is a massive growth in the amount of computing power in today’s world and with the help of the same computing power, simulations will help test and simulate scenarios to have real time results. However, the challenge does not end here, there is a much bigger hurdle caused by the growing complexities of modelling a complete simulation environment. This thesis focuses on providing a solution for modelling a RADAR sensor for a simulation environment. This research presents a RADAR modeling technique suitable for autonomous vehicle simulation environment using open-source utilities. This study proposes to customize an onboard LiDAR model to the specification of a desired RADAR field of view, resolution, and range and then utilizes a density-based clustering algorithm to generate the RADAR output on an open-source graphical engine such as Unreal Engine (UE). High fidelity RADAR models have recently been developed for proprietary simulation platforms such as MATLAB under its automated driving toolbox. However, open-source RADAR models for open-source simulation platform such as UE are not available. This research focuses on developing a RADAR model on UE using blueprint visual scripting for off-road vehicles. The model discussed in the thesis uses 3D pointcloud data generated from the simulation environment and then clipping the data according to the FOV of the RADAR specification, it clusters the points generated from an object using DBSCAN. The model gives the distance and azimuth to the object from the RADAR sensor in 2D. This model offers the developers a base to build upon and help them develop and test autonomous control algorithms requiring RADAR sensor data. Preliminary simulation results show promise for the proposed RADAR model.

Upcoming missions to explore planetary bodies in the solar system will require accurate position and velocity data during descent in order to land safely at a predesignated site. A Doppler lidar instrument could provide measurements of the altitude, attitude, and velocity of the landing vehicle to supplement the data collected by other instruments. A flexible simulation tool would aid the tasks of designing and testing the functionality of such an instrument. LadarSIM is a robust parameterized simulation tool developed for time of flight lidar at Utah State University's Center for Advanced Imaging Ladar. This thesis outlines how LadarSIM was modified to include a simulation of Doppler lidar. A study is performed using LadarSIM to determine the effects of varying certain parameters of a Doppler lidar system. Point clouds of landing scenarios generated by the simulation with different scanning patterns are shown.

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Studies have not been conducted examining the influence of the spatial distribution of LiDAR-derived tree measuresments and their affects the predictive ability of LiDAR-derived forest metrics as input for growth-and-yield analysis on individual trees. This study addresses both of these voids in current knowledge and determines the suitability, concerns and application of LiDAR for time-series analysis, specifically forest growth-and-yield. LiDAR datasets of the same site acquired in 1999, 2000, 2002, and 2006 by different vendors using different specifications were utilized in this study. Directional differences of Lidar-identified tree top locations were examined. Minimal location differences were noted, but no bias occurred. Differences in locations appeared to be from environmental effects such as wind. Improvements on individual-tree identification using a time-series analysis approach were implemented. The treeinding model was improved with a Boolean decision rule yielding significant differences in stand density calculations in 1.4 m spacing plots and for overall calculations of the 2000 and 2002 LiDAR datasets. Individual tree measurements derived from the 1999 LiDAR data were used to estimate growth to the 2006 data. These growth-and-yield values were compared with field-derived and field-measured values. Significant differences were found between the LiDAR- and field-derived measures of growth-and-yield. These increased over time and were believe to be compounded error from the LiDAR-estimated tree diameters. LiDAR datasets can be correlated to previous LiDAR datasets of the same area with very little effort. LiDAR tree identification can be improved using decision criteria based on subsequent LiDAR datasets of the same area. The ability to track individual trees by location over time using LiDAR could yield large datasets to potentially improve growth-and-yield modeling efforts and other stand characterization procedures.

Imagine a technology that automatically creates a full 3D thermal model of an environment and detects temperature peaks in it. For better orientation in the model it is enhanced with color information. The current state of the art for analyzing temperature related issues is thermal imaging. It is relevant for energy efficiency but also for securing important infrastructure such as power supplies and temperature regulation systems. Monitoring and analysis of the data for a large building is tedious as stable conditions need to be guaranteed for several hours and detailed notes about the pose and the environment conditions for each image must be taken. For some applications repeated measurements are necessary to monitor changes over time. The analysis of the scene is only possible through expertise and experience. This thesis proposes a robotic system that creates a full 3D model of the environment with color and thermal information by combining thermal imaging with the technology of terrestrial laser scanning. The addition of a color camera facilitates the interpretation of the data and allows for other application areas. The data from all sensors collected at different positions is joined in one common reference frame using calibration and scan matching. The first part of the thesis deals with 3D point cloud processing with the emphasis on accessing point cloud data efficiently, detecting planar structures in the data and registering multiple point clouds into one common coordinate system. The second part covers the autonomous exploration and data acquisition with a mobile robot with the objective to minimize the unseen area in 3D space. Furthermore, the combination of different modalities, color images, thermal images and point cloud data through calibration is elaborated. The last part presents applications for the the collected data. Among these are methods to detect the structure of building interiors for reconstruction purposes and subsequent detection and classification of windows. A system to project the gathered thermal information back into the scene is presented as well as methods to improve the color information and to join separately acquired point clouds and photo series. A full multi-modal 3D model contains all the relevant geometric information about the recorded scene and enables an expert to fully analyze it off-site. The technology clears the path for automatically detecting points of interest thereby helping the expert to analyze the heat flow as well as localize and identify heat leaks. The concept is modular and neither limited to achieving energy efficiency nor restricted to the use in combination with a mobile platform. It also finds its application in fields such as archaeology and geology and can be extended by further sensors. / Man stelle sich eine Technologie vor, die automatisch ein vollständiges 3D-Thermographiemodell einer Umgebung generiert und Temperaturspitzen darin erkennt. Zur besseren Orientierung innerhalb des Modells ist dieses mit Farbinformationen erweitert. In der Analyse temperaturrelevanter Fragestellungen sind Thermalbilder der Stand der Technik. Darunter fallen Energieeffizienz und die Sicherung wichtiger Infrastruktur, wie Energieversorgung und Systeme zur Temperaturregulierung. Die Überwachung und anschließende Analyse der Daten eines großen Gebäudes ist aufwändig, da über mehrere Stunden stabile Bedingungen garantiert und detaillierte Aufzeichnungen über die Aufnahmeposen und die Umgebungsverhältnisse für jedes Wärmebild erstellt werden müssen. Einige Anwendungen erfordern wiederholte Messungen, um Veränderungen über die Zeit zu beobachten. Eine Analyse der Szene ist nur mit Erfahrung und Expertise möglich. Diese Arbeit stellt ein Robotersystem vor, das durch Kombination von Thermographie mit terrestrischem Laserscanning ein vollständiges 3D Modell der Umgebung mit Farb- und Temperaturinformationen erstellt. Die ergänzende Farbkamera vereinfacht die Interpretation der Daten und eröffnet weitere Anwendungsfelder. Die an unterschiedlichen Positionen aufgenommenen Daten aller Sensoren werden durch Kalibrierung und Scanmatching in einem gemeinsamen Bezugssystem zusammengefügt. Der erste Teil der Arbeit behandelt 3D-Punktwolkenverarbeitung mit Schwerpunkt auf effizientem Punktzugriff, Erkennung planarer Strukturen und Registrierung mehrerer Punktwolken in einem gemeinsamen Koordinatensystem. Der zweite Teil beschreibt die autonome Erkundung und Datenakquise mit einem mobilen Roboter, mit dem Ziel, die bisher nicht erfassten Bereiche im 3D-Raum zu minimieren. Des Weiteren wird die Kombination verschiedener Modalitäten, Farbbilder, Thermalbilder und Punktwolken durch Kalibrierung ausgearbeitet. Den abschließenden Teil stellen Anwendungsszenarien für die gesammelten Daten dar, darunter Methoden zur Erkennung der Innenraumstruktur für die Rekonstruktion von Gebäuden und der anschließenden Erkennung und Klassifizierung von Fenstern. Ein System zur Rückprojektion der gesammelten Thermalinformation in die Umgebung wird ebenso vorgestellt wie Methoden zur Verbesserung der Farbinformationen und zum Zusammenfügen separat aufgenommener Punktwolken und Fotoreihen. Ein vollständiges multi-modales 3D Modell enthält alle relevanten geometrischen Informationen der aufgenommenen Szene und ermöglicht einem Experten, diese standortunabhängig zu analysieren. Diese Technologie ebnet den Weg für die automatische Erkennung relevanter Bereiche und für die Analyse des Wärmeflusses und vereinfacht somit die Lokalisierung und Identifikation von Wärmelecks für den Experten. Das vorgestellte modulare Konzept ist weder auf den Anwendungsfall Energieeffizienz beschränkt noch auf die Verwendung einer mobilen Plattform angewiesen. Es ist beispielsweise auch in Feldern wie der Archäologie und Geologie einsetzbar und kann durch zusätzliche Sensoren erweitert werden.

Three-dimensional (3D) visualization is rapidly becoming an important tool for many engineering projects. Accurate digital representations of terrain and ground features are extremely useful for efficient design, communication and data representation in projects involving land development, transportation planning, hydrologic analysis, environmental impact studies, and much more. Within the scope of terrain modeling lie a wide variety of techniques used to build digital elevation models (DEMs). Each approach has inherent problems and difficulties that can alter the accuracy and usability of the DEM produced. The main objectives of this study are to examine the various methods used for the creation of digital elevation models and make recommendations as to the appropriate techniques to use depending on specific project circumstances. Data sets generated using two of the methods, photogrammetry and LIDAR, are used to build digital terrain models in various software packages for an analysis of data usability and function. The key results of this research project are two DEMs of a real-world transportation study area and a set of conclusions and recommendations that give insight into the exact methods to be used on various projects. The paper ends with two short appendices, the first of which discusses several software packages and their effectiveness in DEM creation and 3-D modeling. The final appendix is a flow chart summarizing the recommendations for the seven DEM creation methods. / Master of Science