Lidar-based scene understanding for autonomous driving using deep learning

With over 1.35 million fatalities related to traffic accidents worldwide, autonomous driving was foreseen at the beginning of this century as a feasible solution to improve security in our roads. Nevertheless, it is meant to disrupt our transportation paradigm, allowing to reduce congestion, pollution, and costs, while increasing the accessibility, efficiency, and reliability of the transportation for both people and goods. Although some advances have gradually been transferred into commercial vehicles in the way of Advanced Driving Assistance Systems (ADAS) such as adaptive cruise control, blind spot detection or automatic parking, however, the technology is far from mature. A full understanding of the scene is actually needed so that allowing the vehicles to be aware of the surroundings, knowing the existing elements of the scene, as well as their motion, intentions and interactions. In this PhD dissertation, we explore new approaches for understanding driving scenes from 3D LiDAR point clouds by using Deep Learning methods. To this end, in Part I we analyze the scene from a static perspective using independent frames to detect the neighboring vehicles. Next, in Part II we develop new ways for understanding the dynamics of the scene. Finally, in Part III we apply all the developed methods to accomplish higher level challenges such as segmenting moving obstacles while obtaining their rigid motion vector over the ground. More specifically, in Chapter 2 we develop a 3D vehicle detection pipeline based on a multi-branch deep-learning architecture and propose a Front (FR-V) and a Bird’s Eye view (BE-V) as 2D representations of the 3D point cloud to serve as input for training our models. Later on, in Chapter 3 we apply and further test this method on two real uses-cases, for pre-filtering moving obstacles while creating maps to better localize ourselves on subsequent days, as well as for vehicle tracking. From the dynamic perspective, in Chapter 4 we learn from the 3D point cloud a novel dynamic feature that resembles optical flow from RGB images. For that, we develop a new approach to leverage RGB optical flow as pseudo ground truth for training purposes but allowing the use of only 3D LiDAR data at inference time. Additionally, in Chapter 5 we explore the benefits of combining classification and regression learning problems to face the optical flow estimation task in a joint coarse-and-fine manner. Lastly, in Chapter 6 we gather the previous methods and demonstrate that with these independent tasks we can guide the learning of higher challenging problems such as segmentation and motion estimation of moving vehicles from our own moving perspective.Con más de 1,35 millones de muertes por accidentes de tráfico en el mundo, a principios de siglo se predijo que la conducción autónoma sería una solución viable para mejorar la seguridad en nuestras carreteras. Además la conducción autónoma está destinada a cambiar nuestros paradigmas de transporte, permitiendo reducir la congestión del tráfico, la contaminación y el coste, a la vez que aumentando la accesibilidad, la eficiencia y confiabilidad del transporte tanto de personas como de mercancías. Aunque algunos avances, como el control de crucero adaptativo, la detección de puntos ciegos o el estacionamiento automático, se han transferido gradualmente a vehículos comerciales en la forma de los Sistemas Avanzados de Asistencia a la Conducción (ADAS), la tecnología aún no ha alcanzado el suficiente grado de madurez. Se necesita una comprensión completa de la escena para que los vehículos puedan entender el entorno, detectando los elementos presentes, así como su movimiento, intenciones e interacciones. En la presente tesis doctoral, exploramos nuevos enfoques para comprender escenarios de conducción utilizando nubes de puntos en 3D capturadas con sensores LiDAR, para lo cual empleamos métodos de aprendizaje profundo. Con este fin, en la Parte I analizamos la escena desde una perspectiva estática para detectar vehículos. A continuación, en la Parte II, desarrollamos nuevas formas de entender las dinámicas del entorno. Finalmente, en la Parte III aplicamos los métodos previamente desarrollados para lograr desafíos de nivel superior, como segmentar obstáculos dinámicos a la vez que estimamos su vector de movimiento sobre el suelo. Específicamente, en el Capítulo 2 detectamos vehículos en 3D creando una arquitectura de aprendizaje profundo de dos ramas y proponemos una vista frontal (FR-V) y una vista de pájaro (BE-V) como representaciones 2D de la nube de puntos 3D que sirven como entrada para entrenar nuestros modelos. Más adelante, en el Capítulo 3 aplicamos y probamos aún más este método en dos casos de uso reales, tanto para filtrar obstáculos en movimiento previamente a la creación de mapas sobre los que poder localizarnos mejor en los días posteriores, como para el seguimiento de vehículos. Desde la perspectiva dinámica, en el Capítulo 4 aprendemos de la nube de puntos en 3D una característica dinámica novedosa que se asemeja al flujo óptico sobre imágenes RGB. Para ello, desarrollamos un nuevo enfoque que aprovecha el flujo óptico RGB como pseudo muestras reales para entrenamiento, usando solo information 3D durante la inferencia. Además, en el Capítulo 5 exploramos los beneficios de combinar los aprendizajes de problemas de clasificación y regresión para la tarea de estimación de flujo óptico de manera conjunta. Por último, en el Capítulo 6 reunimos los métodos anteriores y demostramos que con estas tareas independientes podemos guiar el aprendizaje de problemas de más alto nivel, como la segmentación y estimación del movimiento de vehículos desde nuestra propia perspectivaAmb més d’1,35 milions de morts per accidents de trànsit al món, a principis de segle es va predir que la conducció autònoma es convertiria en una solució viable per millorar la seguretat a les nostres carreteres. D’altra banda, la conducció autònoma està destinada a canviar els paradigmes del transport, fent possible així reduir la densitat del trànsit, la contaminació i el cost, alhora que augmentant l’accessibilitat, l’eficiència i la confiança del transport tant de persones com de mercaderies. Encara que alguns avenços, com el control de creuer adaptatiu, la detecció de punts cecs o l’estacionament automàtic, s’han transferit gradualment a vehicles comercials en forma de Sistemes Avançats d’Assistència a la Conducció (ADAS), la tecnologia encara no ha arribat a aconseguir el grau suficient de maduresa. És necessària, doncs, una total comprensió de l’escena de manera que els vehicles puguin entendre l’entorn, detectant els elements presents, així com el seu moviment, intencions i interaccions. A la present tesi doctoral, explorem nous enfocaments per tal de comprendre les diferents escenes de conducció utilitzant núvols de punts en 3D capturats amb sensors LiDAR, mitjançant l’ús de mètodes d’aprenentatge profund. Amb aquest objectiu, a la Part I analitzem l’escena des d’una perspectiva estàtica per a detectar vehicles. A continuació, a la Part II, desenvolupem noves formes d’entendre les dinàmiques de l’entorn. Finalment, a la Part III apliquem els mètodes prèviament desenvolupats per a aconseguir desafiaments d’un nivell superior, com, per exemple, segmentar obstacles dinàmics al mateix temps que estimem el seu vector de moviment respecte al terra. Concretament, al Capítol 2 detectem vehicles en 3D creant una arquitectura d’aprenentatge profund amb dues branques, i proposem una vista frontal (FR-V) i una vista d’ocell (BE-V) com a representacions 2D del núvol de punts 3D que serveixen com a punt de partida per entrenar els nostres models. Més endavant, al Capítol 3 apliquem i provem de nou aquest mètode en dos casos d’ús reals, tant per filtrar obstacles en moviment prèviament a la creació de mapes en els quals poder localitzar-nos millor en dies posteriors, com per dur a terme el seguiment de vehicles. Des de la perspectiva dinàmica, al Capítol 4 aprenem una nova característica dinàmica del núvol de punts en 3D que s’assembla al flux òptic sobre imatges RGB. Per a fer-ho, desenvolupem un nou enfocament que aprofita el flux òptic RGB com pseudo mostres reals per a entrenament, utilitzant només informació 3D durant la inferència. Després, al Capítol 5 explorem els beneficis que s’obtenen de combinar els aprenentatges de problemes de classificació i regressió per la tasca d’estimació de flux òptic de manera conjunta. Finalment, al Capítol 6 posem en comú els mètodes anteriors i demostrem que mitjançant aquests processos independents podem abordar l’aprenentatge de problemes més complexos, com la segmentació i estimació del moviment de vehicles des de la nostra pròpia perspectiva

Strain partitioning and the seismicity distribution within a transpressive plate boundary : SW Iberia-NW Nubia

Tese de doutoramento, Geologia (Geodinâmica Interna), Universidade de Lisboa, Faculdade de Ciências, 2017The Gulf of Cadiz offshore SW Iberia is an area linked with episodic destructive seismic and tsunamigenic events, such as the M~8.8, 1st November 1755 Lisbon earthquake among others. The association of active faults to this kind of high magnitude event has been extensively studied specially due to the contribution of several international projects for more than two decades. However, the meaning of the persistent small to intermediate magnitude seismicity recognized in this region is still particularly not fully understood. This is, at least, related to the lack of an accurate hypocenter location of these events resulting from an asymmetrical geographical distribution of the permanent seismic network. One of the main purposes of the NEAREST project (Integrated observation from NEAR shore sourcES of Tsunamis: towards an early warning system GOCE, contract n. 037110) was the identification and characterization of seismogenic and tsunamigenic structures in the Gulf of Cadiz area, source region of the Lisbon 1755 earthquake and tsunami. To address this problem 24 broadband Ocean Bottom Seismometers (OBS) and a seafloor multiparametric station GEOSTAR (Geophysical and Oceanographic Station for Abyssal Research) acquired between August 2007 and July 2008 passive seismic data in this region. The results delivered a detailed record of the local seismicity, revealing 3 main clusters of earthquakes, two of them coinciding with the location of the 3 larger instrumental earthquakes in the area: i) the 28th February 1969 (Mw~8.0); ii) the 12th February 2007 (Mw=6.0) and iii) the 17th December 2009 (Mw=5.5). Focal mechanisms show a mixed pattern, mostly strike-slip and reverse dip-slip with a very few normal mechanisms. The results show that of the recorded events are located in the mantle (at depths between 30 and 60 km). This implies the existence of tectonically active structures located much deeper than the ones mapped by Multichannel seismic reflection. A thorough analysis shows that the seismicity clusters are offset with respect to the upper crustal active thrusts. The wide-range solutions of focal mechanisms also imply that the related source processes are complex. This can reflect the interaction of different active geological features, such as faults and rheological boundaries. To understand these new results in the context of the seismotectonics of the Gulf of Cadiz a review of some available geophysical data (reflection and refraction seismic profiles interpretation) in this area is presented as well as novel work on seismic reflection profile IAM GB1 across a rheologic boundary and seismicity cluster. Our study shows that the seismicity clusters are located at faults intersections mapped at the seafloor and shallow crust, suggesting that the crustal tectonic faults are replicated in the lithospheric mantle. These fault interferences are associated with boundaries of lithospheric domains prone to localize stress and seismic strain. Active crustal faults are either locked or move through slow aseismic slip. Frictional slip in crustal faults is probably limited to high magnitude earthquakes. Serpentinization probably induces tectonic decoupling limiting micro-seismicity to depths below the serpentinized layer. It is expected that during highmagnitude events seismic rupture is favored by weakening mechanisms and propagates upwards through the serpentinized layer up to the surface. The results obtained in this work improve our knowledge about the local seismicity and related active faults in the Gulf of Cadiz area, giving a new contribution to access to the seismic hazard in the Nubia-Iberia plate boundary in the Northeast Atlantic Region.O Golfo de Cádis é uma região com uma sismicidade moderada embora se conheça, tanto no registo histórico como instrumental, eventos de elevada magnitude. O sismo de 1 de Novembro de 1755 é um exemplo paradigmático com uma magnitude estimada de 8.8 e um tsunami associado com Mt = 8.5. Já o sismo de 28 de Fevereiro de 1969, é o mais importante registado instrumentalmente, teve uma Ms de 7.9, ao qual esteve associado um pequeno tsunami. Mais recentemente, salientam-se os sismos de 12 de Fevereiro de 2007, com Mw=6.0 e o de 17 de Dezembro de 2009, com Mw =5.5 (EMSCEuropean-Mediterranean Seismological Centre). No entanto, a sismicidade nesta região é descrita como de magnitude baixa a intermédia, com uma distribuição em profundidade acima dos 60 km. Correlacionar esta sismicidade com potenciais estruturas sismogénicas no Golfo de Cádis constituiu um dos objectivos do projecto NEAREST (Integrated observation from NEAR shore sourcES of Tsunamis: towards an early warning system GOCE, contract n. 037110). Neste contexto, foram necessárias uma caracterização e localização mais precisas dos eventos sísmicos ocorridos nesta região, até agora limitadas pelos constrangimentos inerentes à distribuição geográfica das estações permanentes terrestres. Por isso, foi desenvolvida uma campanha de aquisição de dados contínuos utilizando uma rede de sismómetros de fundo do mar. A rede sísmica NEAREST operou de modo contínuo num período de 11 meses, entre Agosto de 2007 Julho de 2008, integrando 24 sismómetros de fundo do mar (OBS) e uma estação multiparamétrica- GEOSTAR. Durante as campanhas de colocação e recuperação dos instrumentos, as manipulações dos OBS e GEOSTAR estiveram a cargo do Alfred Wegener Institute for Polarand Marine Research e do Istituto Nazionale di Geofisica e Vulcanologia – INGV, respectivamente. Os OBS foram construídos pela K.U.M. Umwelt- und Meerestechnik Kiel GmbH, Germany e incorporavam sismómetros de banda larga Güralp CMG-40T e um hidrofone. A GEOSTAR é um observatório que integra diversos equipamentos para a recolha de dados geofísicos e oceanográficos em contínuo. Nesta estação estão incluídos um sismómetro de banda larga com 3 componentes e um hidrofone usados nesta campanha. As estações terrestres estão a cargo do Instituto Português do Mar e da Atmosfera (IPMA) e Instituto Dom Luiz (IDL) correspondendo a sismómetros de banda larga também com 3 componentes. Os registos nestas estações foram apenas utilizados para constranger as soluções dos mecanismos focais. Em trabalhos futuros, prevê-se a sua inclusão na localização dos eventos identificados pela rede NEAREST. Durante o período de aquisição foram registados na rede terrestre, para a área delimitada pela rede NEAREST, cerca de 270 sismos locais. Durante o período de funcionamento da rede NEAREST foram identificados cerca de 750 eventos observados em mais de 3 estações. Deste total 590 sismos estavam localizados na área da rede NEAREST. A localização hipocentral foi testada usando diferentes metodologias e modelos de velocidades: a) inversão conjunta das posições hipocentrais e correcções de estações; b) o método das diferenças duplas e c) a inversão conjunta do modelo de velocidades-localizações hipocentrais e correcções de estações. O catálogo final inclui 443 eventos identificados em mais de 6 estações e localizados na área da rede NEAREST. De um modo geral, a maioria dos hipocentros estão localizados a mais de 30 km de profundidade, portanto no manto. As magnitudes locais variam entre 1.2 e 4.8. As localizações epicentrais e hipocentrais baseadas na rede NEAREST divergem das soluções conhecidas para a rede terrestre (providenciadas pelo IPMA), estando deslocadas para SW e sendo mais profundas. A diferença de profundidade pode atingir os 40 km. A campanha do projecto NEAREST permitiu a identificação de uma grande quantidade de eventos não detectada pela rede terrestre. Esta campanha permitiu ainda uma redefinição da distribuição da sismicidade na região, até então considerada difusa. Destes resultados foi possível reconhecer 3 enxames de sismicidade, dois destes coincidentes com 3 dos maiores eventos observados no registo instrumental. Tanto os sismos de 28 de Fevereiro de 1969 (Mw~8.0) como 12 de Fevereiro de 2007 (Mw=6.0) na proximidade da Falha da Ferradura e 17 de Dezembro de 2009 (ML=6.0) na região do canhão de São de Vicente. Os mecanismos focais do catálogo NEAREST são consistentes com estes eventos bem como com soluções de tensores de momento publicadas para esta região. No enxame do canhão de São Vicente é onde estão localizados a maioria dos eventos. Os hipocentros encontram-se a profundidades entre os 20 e os 55 km. A distribuição dos epicentros apresenta um alinhamento ≈ NE-SW ao longo do canhão de São Vicente e prolongando-se para o limite NE da Falha da Ferradura. Os mecanismos focais dominantes são de desligamento e oblíquos, combinando movimento de desligamento com uma menor contribuição de movimento inverso. Foram ainda registados raros eventos em falha normal. A compressão máxima é aproximadamente sub-paralela ao SHmax, com uma direcção ≈NW-SE. Os epicentros localizados no enxame a SW da Falha da Ferradura, tem um alinhamento aproximadamente NW-SE, sub-paralelo à direcção de SHmax regional. Neste enxame os hipocentros são mais profundos localizando-se entre os 30 e os 55km. Os mecanismos focais são na sua maioria de desligamento puro existindo alguns eventos em falha inversa e também raras soluções em falha normal. Importa salientar que as soluções de desligamento apresentam frequentemente um plano subparalelo à orientação das falhas de desligamento SWIM (≈WNW-ESE a E-W). A compressão máxima é aproximadamente NW-SE e NNW-SSE, a W e E do enxame de sismicidade, respectivamente. As direcções de SHmax são mais uma vez coincidentes com a direção de compressão máxima. No enxame do Banco do Gorringe maioria dos sismos estão localizados no bordo SW deste relevo submarino, sub-paralelos à falha do Gorringe. Os eventos são menos profundos quando comparados com os outros dois enxames, na sua maioria acima dos 40 km. Os mecanismos focais são na sua maioria de desligamento e em falha inversa. Também neste enxame foram registados alguns sismos em falha normal. A direcção de compressão máxima e o SHmax são NNW-SSE. O facto de estes eventos se localizarem predominantemente no manto constitui um dos principais resultados deste trabalho. Neste contexto, tendo em consideração a profundidade dos eventos sísmicos, a correlação da sismicidade com as estruturas sismogénicas na região do Golfo de Cádis é particularmente complexa. Esta comparação foi desenvolvida com base nos dados de sísmica de reflexão e refração disponíveis. Do nosso estudo resulta que a sismicidade parece estar concentrada em zonas de interferência de falhas localizadas no manto subcrustal litosférico. Estas deverão ser uma replicação do padrão observado a níveis crustais e parecem ser coincidentes com transições entre diferentes domínios litosféricos. Estas zonas de interferência de falhas deverão ser áreas favoráveis à acumulação de tensões e deformação sísmica. As falhas activas crustais deverão estar ou bloqueadas ou movimentar-se de modo assísmico. A movimentação sísmica pode estar associada apenas a sismos de maior magnitude. A existência de níveis serpentinizados no Golfo de Cádis é suportada por dados de sísmica refracção e furos de sondagens profundas. Estes podem funcionar como planos de descolamento para as grandes falhas inversas, acomodando a movimentação asísmica e impedindo a micro-sismicidade de se propagar aos níveis crustais. Durante os sismos de elevada magnitude estes níveis serpentinizados deverão funcionar como zona enfraquecida, de baixo atrito, favorecendo a propagação da ruptura sísmica até à superfície. Os resultados obtidos neste trabalho melhoram o nosso conhecimento sobre a sismicidade e a sua relação com as falhas activas na região do limite de placas litosféricas no Golfo de Cádis, contribuindo para o estudo do risco sísmico associada a sismos devastadores

An Impulse Detection Methodology and System with Emphasis on Weapon Fire Detection

This dissertation proposes a methodology for detecting impulse signatures. An algorithm with specific emphasis on weapon fire detection is proposed. Multiple systems in which the detection algorithm can operate, are proposed. In order for detection systems to be used in practical application, they must have high detection performance, minimizing false alarms, be cost effective, and utilize available hardware. Most applications require real time processing and increased range performance, and some applications require detection from mobile platforms. This dissertation intends to provide a methodology for impulse detection, demonstrated for the specific application case of weapon fire detection, that is intended for real world application, taking into account acceptable algorithm performance, feasible system design, and practical implementation. The proposed detection algorithm is implemented with multiple sensors, allowing spectral waveband versatility in system design. The proposed algorithm is also shown to operate at a variety of video frame rates, allowing for practical design using available common, commercial off the shelf hardware. Detection, false alarm, and classification performance are provided, given the use of different sensors and associated wavebands. The false alarms are further mitigated through use of an adaptive, multi-layer classification scheme, leading to potential on-the-move application. The algorithm is shown to work in real time. The proposed system, including algorithm and hardware, is provided. Additional systems are proposed which attempt to complement the strengths and alleviate the weaknesses of the hardware and algorithm. Systems are proposed to mitigate saturation clutter signals and increase detection of saturated targets through the use of position, navigation, and timing sensors, acoustic sensors, and imaging sensors. Furthermore, systems are provided which increase target detection and provide increased functionality, improving the cost effectiveness of the system. The resulting algorithm is shown to enable detection of weapon fire targets, while minimizing false alarms, for real-world, fieldable applications. The work presented demonstrates the complexity of detection algorithm and system design for practical applications in complex environments and also emphasizes the complex interactions and considerations when designing a practical system, where system design is the intersection of algorithm performance and design, hardware performance and design, and size, weight, power, cost, and processing

Learning the link between Albedo and reflectance: Machine learning-based prediction of hyperspectral bands from CTX images

The instruments of the Mars Reconnaissance Orbiter (MRO) provide a large quantity and variety of imagining data for investigations of the Martian surface. Among others, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures visible to infrared reflectance across several hundred spectral bands. However, Mars is only partially covered with targeted CRISM at full spectral and spatial resolution. In fact, less than one percent of the Martian surface is imaged in this way. In contrast, the Context Camera (CTX) onboard the MRO delivers images with a higher spatial resolution and the image data cover almost the entire Martian surface. In this work, we examine to what extent machine learning systems can learn the relation between morphology, albedo and spectral composition. To this end, a dataset of 67 CRISM-CTX image pairs is created and different deep neural networks are trained for the pixel-wise prediction of CRISM bands solely based on the albedo information of a CTX image. The trained models enable us to estimate spectral bands across large areas without existing CRISM data and to predict the spectral composition of any CTX image. The predictions are qualitatively similar to the ground-truth spectra and are also able to recover finer grained details, such as dunes or small craters

3D Perception Based Lifelong Navigation of Service Robots in Dynamic Environments

Lifelong navigation of mobile robots is to ability to reliably operate over extended periods of time in dynamically changing environments. Historically, computational capacity and sensor capability have been the constraining factors to the richness of the internal representation of the environment that a mobile robot could use for navigation tasks. With affordable contemporary sensing technology available that provides rich 3D information of the environment and increased computational power, we can increasingly make use of more semantic environmental information in navigation related tasks.A navigation system has many subsystems that must operate in real time competing for computation resources in such as the perception, localization, and path planning systems. The main thesis proposed in this work is that we can utilize 3D information from the environment in our systems to increase navigational robustness without making trade-offs in any of the real time subsystems. To support these claims, this dissertation presents robust, real world 3D perception based navigation systems in the domains of indoor doorway detection and traversal, sidewalk-level outdoor navigation in urban environments, and global localization in large scale indoor warehouse environments.The discussion of these systems includes methods of 3D point cloud based object detection to find respective objects of semantic interest for the given navigation tasks as well as the use of 3D information in the navigational systems for purposes such as localization and dynamic obstacle avoidance. Experimental results for each of these applications demonstrate the effectiveness of the techniques for robust long term autonomous operation

Multi-Scale Integral Invariants for Robust Character Extraction from Irregular Polygon Mesh Data

Hunderttausende von antiken Dokumenten in Keilschrift befinden sich in Museen, und täglich werden weitere bei archäologischen Grabungen gefunden. Die Auswertung dieser Dokumente ist wesentlich für das Verständnis der Herkunft von Kultur, Gesetzgebung und Religion. Die Keilschrift ist eine Handschrift und wurde in den Jahrtausenden vor Christi Geburt im gesamten alten Orient benutzt. Der Name leitet sich von den keilförmigen Eindrücken eines Schreibgriffels in den weichen Beschreibstoff Ton ab. Das Anfertigen von Handzeichnungen und Transkriptionen dieser Tontafeln ist eine langwierige Aufgabe und verlangt nach Unterstützung mittels automatisierter rechnergestützter Verfahren. Das Ziel dieser Arbeit ist die präzise Extraktion von Schriftzeichen mit variablen Formen in 3D. Die für die Merkmalsextraktion aus 2D-Mannigfaltigkeiten in 3D entscheidenden Schritte sind Kantenerkennung und Segmentierung. Robuste Techniken in der Signalverarbeitung und dem Shape Matching benutzen hierfür Integralinvarianten in 2D. In aktuellen Arbeiten werden die Integralinvarianten grob geschätzt, um wenige prägnante Merkmale zu finden, mit denen sich zerbrochene 3D-Objekte zusammensetzen lassen. Mit dem Ziel der exakten Bestimmung der 3D-Formen von Zeichen, wurde die aus der Bildverarbeitung und Mustererkennung bekannte Verarbeitungskette an 3D-Modelle angepasst. Diese Modelle bestehen aus Millionen von Messpunkten, die mit optischen 3D-Scannern aufgenommen werden. Die Punkte approximieren Mannigfaltigkeiten durch ein irreguläres Dreiecksnetz. Verschiedene Typen von integralinvarianten Filtern in mehreren Skalen führen zu verschiedenen hochdimensionalen Merkmalsräumen. Faltungen und kombinierte Metriken werden auf die Merkmalsräume angewandt, um Zusammenhangskomponenten zu bestimmen. Diese Komponenten stellen die Zeichen genauer als die Messauflösung dar. Parallel zum Design der Algorithmen werden die Eigenschaften der verschiedenen Integralinvarianten analysiert. Die Interpretation der Filterergebnisse sind von großem Nutzen zur Bestimmung von robusten Krümmungsmaßen und zur Segmentierung. Die Extraktion von Keilschriftzeichen wird mit einer Voronoi basierten Berechnung von minimalen normalisierbaren Vektordarstellungen vervollständigt. Diese Darstellung ist eine wichtige Grundlage für die Paläographie. Weitere Abstraktion und Normalisierung der Darstellung führt zur Zeichenerkennung. Die Einbettung der Algorithmen in das neu entworfene mehrschichtige GigaMesh Software Framework erlaubt eine Vielzahl von Anwendungen. Die Algorithmen nutzen den Speicher effektiv und die Verarbeitungskette ist parallelisiert. Die konfigurierbare Verarbeitungskette hat nur einen relevanten Parameter, nämlich die maximale Größe der zu erwartenden Merkmale. Die vorgestellten Verfahren wurden an Hunderten von Keilschrifttafeln, so wie weiteren realen und synthetischen Objekten getestet.Repräsentative Ergebnisse sowie Aufwands- und Genauigkeitsabschätzung der Algorithmen werden gezeigt. Ein Ausblick auf künftige Erweiterungen und Integralinvarianten in höheren Dimensionen gegeben

Dijet resonance searches for dark matter mediators using the ATLAS detector

Dark matter is abundant in the universe, yet its composition and nature are unknown. If dark matter consists of new particles with nonzero interactions with Standard Model particles, it could be produced and detected or inferred by collider experiments. This thesis describes searches for dark matter models with new spin-1 force mediators coupling dark matter to quarks, using the ATLAS detector at the LHC. The studies target processes where a mediator is resonantly produced and decays back into two quarks, leaving two collimated jets of particles in the detector, and use \sqrt{s} = 13 \TeV data recorded by ATLAS between 2015 and 2017. No evidence for new physics was seen, and exclusion limits were set on benchmark models. Additionally, we describe how such limits can be reinterpreted in the contexts of other models to provide further constraints. In order to conduct these searches, well-calibrated jets are needed, as well as good understanding of detector performance. This thesis also describes several tools and studies aimed at improving the jet physics performance of ATLAS. In particular, methods for studying the jet energy calibration and resolution are presented

Uncertainty quantification and numerical methods in charged particle radiation therapy

Radiation therapy is applied in approximately 50% of all cancer treatments. To eliminate the tumor without damaging organs in the vicinity, optimized treatment plans are determined. This requires the calculation of three-dimensional dose distributions in a heterogeneous volume with a spatial resolution of 2-3mm. Current planning techniques use multiple beams with optimized directions and energies to achieve the best possible dose distribution. Each dose calculation however requires the discretization of the six-dimensional phase space of the linear Boltzmann transport equation describing complex particle dynamics. Despite the complexity of the problem, dose calculation errors of less than 2% are clinically recommended and computation times cannot exceed a few minutes. Additionally, the treatment reality often differs from the computed plan due to various uncertainties, for example in patient positioning, the acquired CT image or the delineation of tumor and organs at risk. Therefore, it is essential to include uncertainties in the planning process to determine a robust treatment plan. This entails a realistic mathematical model of uncertainties, quantification of their effect on the dose distribution using appropriate propagation methods as well as a robust or probabilistic optimization of treatment parameters to account for these effects. Fast and accurate calculations of the dose distribution including predictions of uncertainties in the computed dose are thus crucial for the determination of robust treatment plans in radiation therapy. Monte Carlo methods are often used to solve transport problems, especially for applications that require high accuracy. In these cases, common non-intrusive uncertainty propagation strategies that involve repeated simulations of the problem at different points in the parameter space quickly become infeasible due to their long run-times. Quicker deterministic dose calculation methods allow for better incorporation of uncertainties, but often use strong simplifications or admit non-physical solutions and therefore cannot provide the required accuracy. This work is concerned with finding efficient mathematical solutions for three aspects of (robust) radiation therapy planning: 1. Efficient particle transport and dose calculations, 2. uncertainty modeling and propagation for radiation therapy, and 3. robust optimization of the treatment set-up

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described