Expectations for turbine-mounted lidar are increasing. The installation of lidars in wind turbine nacelles for measuring incoming winds, preventing wind gusts and increasing energy productions is after recently studies, technically and economically feasible. Among available lidar types, the most studied were continuous wave lidars because they were the most reliable apparatus when this initiative began. However, after studying technical considerations and checking commercial lidars, it was found that pulsed lidarslead this technology due to their promising results. The purpose of this report is to fill the gap between the interest in this technology and the absence of any academic papers that analyzes continuous-wave and pulsed lidars forthe mounted lidar concept. Hence, this report discusses the importance of turbine mounted lidars for wind power industry, different possible configurations and explains why specifically pulsed lidars are becoming more important for the mounted lidarmarket.

A Digital Terrain Model (DTM) is an important topographic product, required in many applications. Data needed to create a DTM was traditionally obtained via land surveying, however this method can be costly and time consuming depending on the size of the geographic area. Over time, the land surveying was partially replaced by photogrammetry. Today, airborne Light Detection and Ranging (LiDAR) has become another powerful alternative that collect 3D point clouds for digital surface models (DSM) acquisition. LiDAR is especially useful when dealing with heavily vegetated areas using a canopy penetration feature of laser pulse. Nowadays, LiDAR plays an important role in DTM generation. This thesis presents a hierarchical recovery method to generate DTMs from a cloud of 3D points composed of “single returns” and “multiple returns” from laser pulses using the idea of layering. The proposed method will begin by registering the last return points, then layering them. The layering is done by dividing the points into different height layers and assigning layer numbers to each point. The layer numbers are used as a comparison feature in a later identification process. Then a series of rasterized pyramid levels, which consists of the lowest points in each cell, are generated. After layering, outliers are removed; cells in the top level are assumed as terrain points and used as references for identifying cells in the second level. The identification process will identify the cells of the second level into terrain cells and off-terrain cells, and an interpolation will then occur in the cells which identified as off-terrain. The interpolated level will be used as references for the next level and the same process is then repeated for each level that comes after. Once this process has been completed for the bottom level, the proposed method adjusts the results based on the first return feedback, followed by another interpolation. As a result, the final DTM is produced. The developed method is data driven, and does not assume a prior knowledge about the scene complexity. The proposed method was tested with three airborne LiDAR datasets, covering different terrain types and filtering difficulties. Results illustrated that the proposed method can perform well for areas of flat terrain or gentle slope A comparative study was conducted over existing filters and showed that results of the proposed method has similar accuracy in above mentioned area and faster speed than two comparing algorithms.

Zugl.: München, University, Diss., 2006. / ISRN DLR-FB-2006-12.

Thèse (M.Sc.)--Université Laval, 2007. / Bibliogr.: f. 94-102. Publié aussi en version électronique dans la Collection Mémoires et thèses électroniques.

Zugl.: Hannover, Universiẗat, Diss., 2008. / Auch als: Deutsche Geodätische Kommission bei der Bayerischen Akademie der Wissenschaften : Reihe C, Dissertationen ; 622.

Thèse (M.Sc.)--Université Laval, 2009. / Titre de l'écran-titre (visionné le 13 janvier 2010). Bibliogr.

La technique Lidar Radar est couramment utilisée pour la détection de cibles immergées dans des eaux peu profondes inférieures à quelques dizaines de mètres. Cette technique repose sur l’envoi d’un signal modulé associé, à la réception avec un filtre passe bande autour de la fréquence de modulation. Cette technique requiert ainsi un signal optique bleu vert, intense, modulé à des fréquences radar. Nous présentons dans cette thèse de nouvelles architectures de modulateur parfaitement adaptées à cette technique. La 1ère architecture est constituée d’une cavité externe comportant un doubleur intracavité. Cette architecture est couplée à une source laser picoseconde infrarouge (1064 nm). Les résultats ont montré que ce modulateur permet de générer un signal vert (532 nm), intense (5 mJ) et stable en fréquence. Grâce à la source utilisée, le signal modulé en sortie de l’émetteur (source laser et modulateur) ne dure que quelques nanosecondes. Ceci permet d’utiliser la méthode de « range-gating » pour obtenir une précision sur la localisation de la cible. Néanmoins, ce dispositif présente l’inconvénient d’avoir une bande passante du signal émis fixe. Nous avons donc développé une deuxième architecture du modulateur, permettant d’accorder facilement la bande passante du signal émis. Cette configuration repose sur le comportement polarimétrique des composants optiques afin de changer la largeur de bande passante du signal. Nous avons montré que ce modulateur permet de délivrer, un signal intense (jusqu’à 2,9 mJ), court (quelques nanosecondes), à 532 nm, modulé à des fréquences radar, stable en fréquence et accordable en bande passante. / The Lidar Radar technique is commonly used for submerged target detection in shallow waters less than a few tens of meters. This technique is based on sending a modulated signal, associated with a bandpass filter around the modulation frequency after detection. This technique requires an intense blue-green optical signal modulated at radar frequencies. We present in this thesis new modulator architectures perfectly adapted to this technique. The first architecture consists of an external cavity with an intracavity SHG stage. This architecture is coupled to an infrared picosecond laser source (1064 nm). The results showed that this modulator makes it possible to generate a green signal (532 nm), intense (5 mJ) and stable in frequency. Thanks to the source used, the signal modulated at the output of the transmitter (laser source and modulator) lasts only a few nanoseconds. This makes it possible to use the "range-gating" method to precisely locate the target.Nevertheless, this device has the disadvantage of a fixed bandwidth. We have therefore developed a second architecture of the modulator, allowing to easily tune the bandwidth of the transmitted signal. This configuration is based on the polarimetric behavior of the optical components in order to change the bandwidth of the signal. We have shown that this modulator can deliver , an intense signal (up to 2.9 mJ), short (a few nanoseconds), at 532 nm, modulated at mirowave frequencies, stable in frequency and tunable in bandwidth.

The development of a fleet of flexible and ruggedized unmanned ground vehicles for use in autonomy and distributed sensing research has resulted in a mature platform with proven capabilities. Each Mapping Autonomous Ground Vehicle (MAGV) is capable of travel on- and off-road, speeds up to 10 mph, and its sturdy construction with a rugged suspension cushions onboard instruments from vibrations. The large battery capacity can sustain at least eight hours of hard use, including powering all onboard electronics. The MAGV is fitted with a high accuracy GPS/INS system for centimeter-accuracy localization and a powerful but compact onboard computer. The integrated wireless communications allow high-bandwidth data communication between the MAGV fleet and a base station. The platform can additionally be fitted with a wide array of sensors, including LIDAR and stereovision cameras, and is designed with ample space to allow the mounting of any future data gathering devices. The platform has already taken a central role in the development of new algorithms for map creation with modern sensing technology, and was deployed to collect data for the demonstration of the map estimation algorithms outlined in this thesis. A wavelet basis combined with a state estimator is demonstrated to be effective for approximating a global map of a given area with complex features. The recursive least squares state estimator is highly effective at rejecting transient features, such as pedestrians frequently passing through the field of view, while retaining the shape of the walls and terrain features. The ability to vary the map resolution allows the mapping station to trade detail for a faster map update processing time. In its current implementation, the global map estimator supports the acquisition and integration of data from multiple simultaneous mobile sources. Because each scan is registered using the position of the vehicle when it is recorded, there is no difference between receiving all data from a single agent, or multiple agents working cooperatively gathering data in the same area. The wavelet basis also offers several opportunities for reducing communications overhead through data compression. In particular, we have demonstrated that simple thresholding of the least significant wavelet coefficients results in a significant reduction in data size with no noticeable reduction in fidelity of the reconstructed map estimate. / Master of Science

Change detection is amongst the most effective critical examination methods used in remote sensing technology. In this research, new methods are proposed for building and vegetation change detection using only LiDAR data without using any other remotely sensed data. Two LiDAR datasets from 2009 and 2013 will be used in this research. These datasets are provided by the City of Surrey. A Parcel map which shows parcels in the study area will be also used in this research because the objective of this research is detecting changes based on parcels. Different methods are applied to detect changes in buildings and vegetation respectively. Three attributes of object –slope, building volume, and building height are derived and used in this study. Changes in buildings are not only detected but also categorized based on their attributes. In addition, vegetation change detection is performed based on parcels. The output shows parcels with a change of vegetation. Accuracy assessment is done by using measures of completeness, correctness, and quality of extracted regions. Accuracy assessments suggest that building change detection is performed with better results.

This dissertation introduces an innovative technique to extract ground elevation models using smallootprint LIDAR data. This technique consists of a preprocessing step, ground modeling, and interpolation. In the preprocessing step, much of the non-terrain points are eliminated using a histogram-based clustering technique. Then, in the ground modeling stage, the information such as elevation and slope between nearest neighbor points is extracted. This step corresponds to an outlier detection process. In this stage, residuals and gradient indices for elevation and slope, are introduced. These indices are investigated for a constructed 95% confidence interval to discard the remaining non-terrain points. Finally, using spline interpolation, a smooth ground surface is generated. Experimental results show that the presented technique is more robust and yields better results compared to existing techniques, such as linear prediction, modified linear prediction, and adaptive smoothing, in terms of the root mean squared error, absolute mean, and absolute standard deviation. Furthermore, the possibility of using a ground trend model developed from specific tree height measurements is investigated. For this analysis, a statistical regression analysis model is used. Performing this analysis, a 0.63 R-squared value is obtained. This result indicates that the LIDAR ground surface obtained from the presented algorithm is related to the true ground surface.