Orchards demand large areas of land, thus they are often situated far from major population centres. As a result it is often difficult to obtain the necessary personnel, limiting both growth and productivity. However, if autonomous robots could be integrated into the operation of the orchard, the manpower demand could be reduced. A key problem for any autonomous robot is localisation; how does the robot know where it is? In agriculture robots, the most common approach is to use GPS positioning. However, in an orchard environment, the dense and tall vegetation restricts the usage to large robots that reach above the surroundings. In order to enable the use of smaller robots, it is instead necessary to use a GPS independent system. However, due to the similarity of the environment and the lack of strong recognisable features, it appears unlikely that typical non-GPS solutions will prove successful. Therefore we present a GPS independent localisation system, specifically aimed for orchards, that utilises the inherent structure of the surroundings. Furthermore, we examine and individually evaluate three related sub-problems. The proposed system utilises a 3D point cloud created from a 2D LIDAR and the robot’s movement. First, we show how the data can be segmented into individual trees using a Hidden Semi-Markov Model. Second, we introduce a set of descriptors for describing the geometric characteristics of the individual trees. Third, we present a robust localisation method based on Hidden Markov Models. Finally, we propose a method for detecting segmentation errors when associating new tree measurements with previously measured trees. Evaluation shows that the proposed segmentation method is accurate and yields very few segmentation errors. Furthermore, the introduced descriptors are determined to be consistent and informative enough to allow localisation. Third, we show that the presented localisation method is robust both to noise and segmentation errors. Finally it is shown that a significant majority of all segmentation errors can be detected without falsely labeling correct segmentations as incorrect. / Eftersom fruktodlingar kräver stora markområden är de ofta belägna långt från större befolkningscentra. Detta gör det svårt att finna tillräckligt med arbetskraft och begränsar expansionsmöjligheterna. Genom att integrera autonoma robotar i drivandet av odlingarna skulle arbetet kunna effektiviseras och behovet av arbetskraft minska. Ett nyckelproblem för alla autonoma robotar är lokalisering; hur vet roboten var den är? I jordbruksrobotar är standardlösningen att använda GPS-positionering. Detta är dock problematiskt i fruktodlingar, då den höga och täta vegetationen begränsar användandet till större robotar som når ovanför omgivningen. För att möjliggöra användandet av mindre robotar är det istället nödvändigt att använda ett GPS-oberoende lokaliseringssystem. Detta problematiseras dock av den likartade omgivningen och bristen på distinkta riktpunkter, varför det framstår som osannolikt att existerande standardlösningar kommer fungera i denna omgivning. Därför presenterar vi ett GPS-oberoende lokaliseringssystem, speciellt riktat mot fruktodlingar, som utnyttjar den naturliga strukturen hos omgivningen.Därutöver undersöker vi och utvärderar tre relaterade delproblem. Det föreslagna systemet använder ett 3D-punktmoln skapat av en 2D-LIDAR och robotens rörelse. Först visas hur en dold semi-markovmodell kan användas för att segmentera datasetet i enskilda träd. Därefter introducerar vi ett antal deskriptorer för att beskriva trädens geometriska form. Vi visar därefter hur detta kan kombineras med en dold markovmodell för att skapa ett robust lokaliseringssystem.Slutligen föreslår vi en metod för att detektera segmenteringsfel när nya mätningar av träd associeras med tidigare uppmätta träd. De föreslagna metoderna utvärderas individuellt och visar på goda resultat. Den föreslagna segmenteringsmetoden visas vara noggrann och ge upphov till få segmenteringsfel. Därutöver visas att de introducerade deskriptorerna är tillräckligt konsistenta och informativa för att möjliggöra lokalisering. Ytterligare visas att den presenterade lokaliseringsmetoden är robust både mot brus och segmenteringsfel. Slutligen visas att en signifikant majoritet av alla segmenteringsfel kan detekteras utan att felaktigt beteckna korrekta segmenteringar som inkorrekta.

Magistro baigiamajam darbui pasirinkta tema yra Trimačiai objektai: atvaizdavimo ir deformacijos algoritmai. Ši tema nagrinėja paviršiaus rekonstrukciją iš taškų debesies ir galimybes pritaikyti paviršiaus deformacijos algoritmus. Analizės etapo metu išsiaiškinta, kad pagrindinė paviršiaus atstatymo iš taškų debesies problema yra lėtas algoritmų veikimas. Šiame darbe siūlomas atvirkštinės inžinerijos metodas, veikiantis 2D Delaunay trianguliacijos pagrindu. Pateikiami algoritmai padalina taškų debesį į kelias dalis, tada iš trimatės erdvės taškų debesies dalys yra transformuojamos į dvimatę erdvę, suskaičiuojama 2D Delaunay trianguliacija ir gautas trikampių tinklelis vėl transformuojamas į trimatę erdvę. Taip pat pateikiamos teorinės galimybės gautą paviršių transformuoti jau žinomu algoritmu. Po algoritmų praktinio įgyvendinimo buvo nustatyta, kad jie veikia taip kaip tikėtasi, rezultatas gaunamas greičiau nei naudojant kitus žinomus algoritmus. Taip pat buvo pastebėta, kad 2D Delaunay trianguliaciją geriau naudoti kai taškų skaičius taškų debesyje yra labai didelis, o kai taškų skaičius neviršija 2000 geriau naudoti 3D Delaunay trianguliaciją. / The chosen theme of the Master of Science degree paper is “Three dimensional objects: visualization and deformation algorithms“. This subject considers surface reconstruction from point clouds and the possibilities to apply surface deformation algorithms. During the analysis phase we found that the main problem of the algorithms of surface reconstruction from scanned point clouds is the lack of speed. So in this paper a method, based on 2D Delaunay triangulation, for reverse engineering is proposed. This method divides point clouds into several parts, and then maps all the points of those point cloud parts to the plane. Then a 2D Delaunay triangulation is computed and the mesh is mapped back to the point cloud. We also give theoretical possibilities to apply a known algorithm for surface deformation. During the implementation phase we found that our algorithms work as expected, but quicker than the other methods proposed earlier. We also noticed that it’s better to use 2D Delaunay triangulation for bigger point clouds and 3D Delaunay triangulation for point clouds, which contains no more than approximately 2000 points.

Recent advances in Light Detection and Ranging (LIDAR) technology and integration have resulted in vehicle-borne platforms for urban LIDAR scanning, such as Terrapoint Inc.'s TITAN system. Such technology has lead to an explosion in ground LIDAR data. The large size of such mobile urban LIDAR data sets, and the ease at which they may now be collected, has shifted the bottleneck of creating abstract urban models for Geographical Information Systems (GIS) from data collection to data processing. While turning such data into useful models has traditionally relied on human analysis, this is no longer practical. This thesis outlines a methodology for automatically recovering the necessary information to create abstract urban models from mobile urban LIDAR data using computer vision methods. As an integral part of the methodology, a novel scale-based interest operator is introduced (Di erence of Normals) that is e cient enough to process large datasets, while accurately isolating objects of interest in the scene according to real-world parameters. Finally a novel localized object recognition algorithm is introduced (Local Potential Well Space Embedding), derived from a proven global method for object recognition (Potential Well Space Embedding). The object recognition phase of our methodology is discussed with these two algorithms as a focus. / Thesis (Master, Computing) -- Queen's University, 2010-03-01 12:26:34.698

Face recognition can be used in various biometric applications ranging from identifying criminals entering an airport to identifying an unconscious patient in the hospital With the introduction of 3-dimensional scanners in the last decade, researchers have begun to develop new methods for 3-D face recognition. This thesis focuses on 3-D face recognition using the one- and two-dimensional Discrete Cosine Transform (DCT) . A feature ranking based dimensionality reduction strategy is introduced to select the DCT coefficients that yield the best classification accuracies. Two forms of 3-D representation are used: point cloud and depth map images. These representations are extracted from the original VRML files in a face database and are normalized during the extraction process. Classification accuracies exceeding 97% are obtained using the point cloud images in conjunction with the 2-D DCT.

Syfte – Syftet för den här studien är att utveckla en metod för att mäta diametern på travat timmers ändträytor i en bild med förvridet perspektiv där ytorna befinner sig på olika djup i förhållande till varandra, för att effektivisera mätning inom skogsindustrin. Metod – Denna studie genomfördes i samarbete med Cind AB, och arbetet var uppdelat i två faser med Design Science Research som forskningsmetod. I Fas 1 rektifierades bilder på ändträytorna manuellt och i Fas 2 utnyttjades ett punktmoln för att uppskatta rektifieringsplanet. Detta gjordes i en stereokamerarigg i skala 1:25 på 139 stockar. Samtliga stockar mättes digitalt i de rektifierade bilderna och manuellt med ett digitalt skjutmått. Ett konfidensintervall för differensen beräknades fram för att bedöma mätnoggrannheten. Resultat – Konfidensintervallet för Fas 1 tyder på att metoden har potential då rektifieringsplanet placeras korrekt, vilket Fas 2 visar är en svår och komplex uppgift. Slutsatser – Den utvecklade metodens mätnoggrannhet uppnådde inte studiens mål på 5% felmarginal. Det skulle dock vara möjligt att mäta ändträytor med god noggrannhet om punktmolnet har tillräckligt hög kvalitet. Begränsningar – Mjukvaran som använder punktmolnet för att rektifiera bilderna är en modifierad version av Cinds proprietära produkt. Datamängden som används i studien samlas endast in via Cinds testrigg. / Purpose – The purpose of this study is to develop a method for measuring the diameter of piled logs on a truck in a picture that has skewed perspective and where the end surfaces are at different depths in relation to each other. The intent of this method is to further streamline log measurement in the logging industry. Method – This study was conducted in collaboration with Cind AB, and the work was split in two phases with Design Science Research as research method. In Phase 1, images with log end surfaces were rectified manually, and in Phase 2 a point cloud was used to estimate the rectification plane. This was done with a stereo camera rig in scale 1:25 on a total of 139 logs. All logs were digitally measured in the rectified images and manually measured with a digital caliper. A confidence interval for the difference was calculated to assess the measurement accuracy. Findings – The confidence interval from Phase 1 indicates that the developed method has potential when the rectification plane is placed correctly, which Phase 2 shows is a difficult and complex task. Conclusions – The developed method did not reach the desired measurement accuracy of 5% margin of error, which means that the goal of the study was not achieved. It would be possible to measure the end surfaces of logs with high precision if the point cloud is of a sufficiently high quality. Limitations – The software that utilizes point cloud information to rectify the images is a modified version of Cind’s proprietary product. The dataset that is used in this study is collected solely through Cind’s test rig.

Room layout generation is the problem of generating a drawing or a digital model of an existing room from a set of measurements such as laser data or images. The generation of floor plans can find application in the building industry to assess the quality and the correctness of an ongoing construction w.r.t. the initial model, or to quickly sketch the renovation of an apartment. Real estate industry can rely on automatic generation of floor plans to ease the process of checking the livable surface and to propose virtual visits to prospective customers. As for the general public, the room layout can be integrated into mixed reality games to provide a better immersiveness experience, or used in other related augmented reality applications such room redecoration. The goal of this industrial thesis (CIFRE) is to investigate and take advantage of the state-of-the art mobile devices in order to automate the process of generating room layouts. Nowadays, modern mobile devices usually come a wide range of sensors, such as inertial motion unit (IMU), RGB cameras and, more recently, depth cameras. Moreover, tactile touchscreens offer a natural and simple way to interact with the user, thus favoring the development of interactive applications, in which the user can be part of the processing loop. This work aims at exploiting the richness of such devices to address the room layout generation problem. The thesis has three major contributions. We first show how the classic problem of detecting vanishing points in an image can benefit from an a-priori given by the IMU sensor. We propose a simple and effective algorithm for detecting vanishing points relying on the gravity vector estimated by the IMU. A new public dataset containing images and the relevant IMU data is introduced to help assessing vanishing point algorithms and foster further studies in the field. As a second contribution, we explored the state of-the-art of real-time localization and map optimization algorithms for RGB-D sensors. Real-time localization is a fundamental task to enable augmented reality applications, and thus it is a critical component when designing interactive applications. We propose an evaluation of existing algorithms for the common desktop set-up in order to be employed on a mobile device. For each considered method, we assess the accuracy of the localization as well as the computational performances when ported on a mobile device. Finally, we present a proof of concept of application able to generate the room layout relying on a Project Tango tablet equipped with an RGB-D sensor. In particular, we propose an algorithm that incrementally processes and fuses the 3D data provided by the sensor in order to obtain the layout of the room. We show how our algorithm can rely on the user interactions in order to correct the generated 3D model during the acquisition process.

Certaines opérations de maintenance sur sites industriels nécessitent une planification à partir de modèles numériques 3D des scènes où se déroulent les interventions. Pour permettre la simulation de ces opérations, les modèles 3D utilisés doivent représenter fidèlement la réalité du terrain. Ces représentations virtuelles sont habituellement construites à partir de nuages de points relevés sur le site, constituant une description métrologique exacte de l’environnement sans toutefois fournir une description géométrique de haut niveau.Il existe une grande quantité de travaux abordant le problème de la reconstruction de modèles 3D à partir de nuages de points, mais peu sont en mesure de fournir des résultats suffisamment fiables dans un contexte industriel et cette tâche nécessite en pratique l’intervention d’opérateurs humains.Les travaux réalisés dans le cadre de cette thèse visent l’automatisation de la reconstruction,avec comme principal objectif la fiabilité des résultats obtenus à l’issu du processus. Au vu de la complexité de ce problème, nous proposons d’exploiter des connaissances et données a priori pour guider la reconstruction. Le premier a priori concerne la compositiondes modèles 3D : en Conception Assistée par Ordinateur (CAO), les scènes industrielles sont couramment décrites comme des assemblages de primitives géométriques simples telles que les plans, sphères, cylindres, cônes, tores, etc. Nous hiérarchisons l’analyse en traitant dans un premier temps les plans et les cylindres, comme un préalable à la détection de stores. On obtient ainsi une description fiable des principaux composants d’intérêt dans les environnements industriels. Nous proposons en outre d’exploiter un certain nombre de règles régissant la manière dont ces primitives s’assemblent en un modèle CAO, basées surdes connaissances ”métier” caractérisant les scènes industrielles que nous traitons. De plus,nous tirons parti d’un modèle CAO existant d´ecrivant une scène similaire à celle que nous souhaitons reconstruire, provenant typiquement de la reconstruction antérieure d’un site semblable au site d’intérêt. Bien que semblables en théorie, ces scènes peuvent présenterdes différences significatives qui s’accentuent au cours de leur exploitation.La méthode que nous développons se fonde sur une formulation Bayésienne du problème de reconstruction : il s’agit de retrouver le modèle CAO le plus probable vis à visdes différentes attentes portées par les données et les a priori sur le modèle à reconstruire. Les diverses sources d’a priori s’expriment naturellement dans cette formulation. Pour permettre la recherche du modèle CAO optimal, nous proposons une approche basée surdes tentatives d’insertion d’objets générés aléatoirement. L’acceptation ou le rejet de ces objets repose ensuite sur l’am´elioration systématique de la solution en cours de construction. Le modèle CAO se construit ainsi progressivement, par ajout et suppression d’objets, jusqu’à obtention d’une solution localement optimale. / 3D models are often used in order to plan the maintenance of industrial environments.When it comes to the simulation of maintenance interventions, these 3D models have todescribe accurately the actual state of the scenes they stand for. These representationsare usually built from 3D point clouds that are huge set of 3D measurements acquiredin industrial sites, which guarantees the accuracy of the resulting 3D model. Althoughthere exists many works addressing the reconstruction problem, there is no solution toour knowledge which can provide results that are reliable enough to be further used inindustrial applications. Therefore this task is in fact handled by human experts nowadays.This thesis aims at providing a solution automating the reconstruction of industrialsites from 3D point clouds and providing highly reliable results. For that purpose, ourapproach relies on some available a priori knowledge and data about the scene to beprocessed. First, we consider that the 3D models of industrial sites are made of simpleprimitive shapes. Indeed, in the Computer Aided Design (CAD) field, this kind of scenesare described as assemblies of shapes such as planes, spheres, cylinders, cones, tori, . . . Ourown work focuses on planes, cylinders and tori since these three kind of shapes allow thedescription of most of the main components in industrial environment. Furthermore, weset some a priori rules about the way shapes should be assembled in a CAD model standingfor an industrial facility, which are based on expert knowledge about these environments.Eventually, we suppose that a CAD model standing for a scene which is similar to theone to be processed is available. This a priori CAO model typically comes from the priorreconstruction of a scene which looks like the one we are interested in. Despite the factthat they are similar theoretically, there may be significant differences between the sitessince each one has its own life cycle.Our work first states the reconstruction task as a Bayesian problem in which we haveto find the most probable CAD Model with respect to both the point cloud and the a prioriexpectations. In order to reach the CAD model maximizing the target probability, wepropose an iterative approach which improves the solution under construction each time anew randomly generated shape is tried to be inserted in it. Thus, the CAD model is builtstep by step by adding and removing shapes, until the algorithm gets to a local maximumof the target probability.

The thesis deals with the possible use of optical data for modeling forest area compared with utilization of airborne laser scanning data. At first these two datasets are compared and causes of differences are explained. Then canopy height models are made and object-oriented classification is applied for separation of vegetation stands. Methodical procedure is suggested for delineation and detection individual trees in forest. Then their height is detected. There are summarized and other possibilities for improvement in detection and delineation of trees. The results show that optical data with resolution about 25 cm are suitable for dermining the characteristics of the forest stands up to individual tree level. The outputs of this research can be used for forest inventory. Key words: aerial imagery, image matching, laser scanning, point cloud, forest inventory

Nos últimos anos, acompanhar o desenvolvimento de uma cultura tem se tornado cada vez mais imprescindível para a tomada de decisões. Sistemas aéreos remotamente pilotados são muito promissores em aplicações de monitoramento. Sua flexibilidade, facilidade de operação e construção relativamente barata os tornam os melhores candidatos para monitorar atividades na agricultura de precisão, onde as reações imediatas de manejo às doenças das plantas, à falta de nutrientes das plantas e às mudanças ambientais são o ponto focal para eficiência e produtividade das plantações. No entanto, no Brasil a utilização desta tecnologia ainda é limitada e o número de publicações científicas sobre o assunto é escasso. No caso específico da cana-de-açúcar, a utilização de aeronave remotamente pilotada (RPA) é bastante promissora e publicações científicas internacionais são limitadas. O objetivo deste trabalho foi avaliar a potencialidade de imagens obtidas a partir de câmeras com diferentes bandas espectrais embarcadas em RPA para obtenção de modelos tridimensionais para estimativa de altura, produtividade e variabilidade espacial. As coletas foram realizadas ao longo da safra 2014/2015, durante o período de um ano. Foi utilizada uma aeronave remotamente pilotada equipada com uma câmera digital com sensibilidade na região espectral do visível (RGB) e outra na região espectral do infravermelho próximo (IVP) sincronizadas com um sistema de navegação global por satélite (GNSS). Este sistema possibilitou a aquisição de imagens com altíssima resolução (3 cm pixel-1) e permitiu a geração de orto-mosaicos e modelos digitais de superfícies (MDS) através de métodos de reconstrução automática em 3D, ajustados por pontos de controle em solo. O RPA seguiu um plano de voo pré-determinado sobre o local do estudo para garantir a aquisição de imagens com cruzamento e sobreposição superior a 90%. O método de validação foi conduzido a partir das medidas de altura obtidas a campo com o auxílio de régua topográfica. Após o processamento das imagens aéreas foi possível a identificação das áreas com ausência de fechamento de dossel, observando também a relação desses locais com o baixo desenvolvimento da altura das plantas ao longo de seu ciclo. A regressão entre os valores da estimativa de altura obtidas com as simulações apresentou erro relativo inferior a 13%, já a estimativa da produtividade apresentou erro na faixa de 6%. A estimativa de altura e produtividade demonstram o alto potencial para o monitoramento e avaliação de talhões de cana-de-açúcar, podendo ser uma ferramenta utilizada no apoio a gestão destas áreas. / In the last few years, monitoring the development of a culture has become increasingly imperative for decision-making. Remotely piloted aircraft systems (RPA) are very promising in monitoring applications. Their flexibility, ease of operation, and relatively inexpensive construction make them the best candidates to monitor precision farming activities where immediate management responses to plant diseases, lack of plant nutrients, and environmental changes are the focal point for efficiency And productivity of plantations. However in Brazil the use of this technology is still limited and the number of scientific publications on the subject is scarce. In the specific case of sugarcane the use of RPA is very promising and international scientific publications are limited. The objective of this work was to evaluate the potentiality of images obtained from cameras with different spectral bands embedded in RPA to obtain three - dimensional models for estimation of height, productivity and spatial variability. The collections were carried out during the 2014/2015 harvest, during a period of one year, using a remotely piloted aircraft equipped with a digital camera with sensitivity in the visible spectral region (RGB) and another in the near infrared spectral region (NIR) Synchronized with a GNSS. This system allowed the acquisition of images with very high resolution (3 cm pixel-1) allowing the generation of ortho-mosaics and digital surface models (DSM), through automatic 3D reconstruction methods adjusted by control points in soil. The RPA followed a pre-determined flight plan on the study site to ensure cross-over and overlapping acquisition of over 90%. The validation method was carried out from the height measurements obtained in the field with the aid of topography. After the aerial images processing, it was possible to identify the areas of crop failure, also observing the relation of these locations with the low development of plant height throughout its cycle. The regression between the values of the height estimation obtained with the simulations resulted in a relative error of less than 13%. The results obtained demonstrate the high potential of this technique for monitoring and evaluation of sugarcane fields, and can be a tool used to support the management of these areas.

Au cours de ces dernières décennies, le développement de Systèmes Mobiles de Cartographie, soutenu par un progrès technologique important, est devenu plus apparent. Il a été stimulé par le besoin grandissant de collecte d'informations géographiques précises sur l'environnement. Nous considérons, au sein de cette thèse, des solutions pour l'acquisition des nuages de points mobiles de qualité topographique (précision centimétrique). Il s'agit, dans cette tâche, de mettre au point des méthodes de qualification des données, et d'en améliorer sur les erreurs systématiques par des techniques d'étalonnage et de recalage adéquates.Nous décrivons une démarche innovante d'évaluation de l'exactitude et/ou de la précision des relevés laser mobiles. Celle-ci repose sur l'extraction et la comparaison des entités linéaires de la scène urbaine. La distance moyenne calculée entre les segments appariés, étant la distance modifiée de Hausdorff, sert à noter les nuages par rapport à des références existantes. Pour l'extraction des arêtes, nous proposons une méthode de détection d'intersections entre segments plans retrouvés via un algorithme de RANSAC enrichi d'une analyse de composantes connexes. Nous envisageons également une démarche de correction de relevés laser mobiles à travers un recalage rigide fondé, lui aussi, sur les éléments linéaires. Enfin, nous étudions la pertinence des segments pour en déduire les paramètres de l'étalonnage extrinsèque du système mobile. Nous testons nos méthodes sur des données simulées et des données réelles acquises dans le cadre du projet TerraMobilita. / Over the past few decades, the development of land-based Mobile Mapping Systems (MMS), supported by significant technological progress, has become more prominent. It has been motivated by the growing need for precise geographic information about the environment. In this thesis, we consider approaches for the acquisition of mobile point clouds with topographic quality (centimeter-level accuracy). The aim is to develop techniques for data quality assessment and improvement. In particular, we eliminate the systematic errors inherent to an MMS data using appropriate calibration and registration methods.We describe a novel approach to assess the accuracy and/or the precision of mobile laser point clouds. It is based on the extraction and comparison of line features detected within the urban scene. The computed average distance between corresponding pairs of line segments, taking advantage of a modified Hausdorff distance, is used to evaluate the MMS data with respect to a reference data set. For edge extraction, we propose a method which relies on the intersections between planes modelled via the RANSAC algorithm refined by an analysis of connected components. We also consider an approach to correct point clouds using a line-based rigid registration method. Finally, we study the use of line segments for estimating the boresight angles of a land-based mobile mapping system. We apply our methods to synthetic data and to real data acquired as part of the TerraMobilita project.