To reduce greenhouse gas emissions in the atmosphere and protect the environment, Swedish cities are heavily invested in sustainable development by developing pedestrian roads and intensifying public transport. Their goal is to make the population less dependent on cars by facilitating accessibility to transit transport. The urban area of ​​Umeå (Sweden), which is experiencing an annual increasing of it population due to urban development, includes 244 km of pedestrian or bike routes and an intensified bus network. The purpose of this paper is to evaluate the walking accessibility of the population at bus stops and to analyze the socio-economic relationship. The use of GIS tools allowed to calculate the proportion of the population and the houses around the bus stop, the average distance accessibility. The use of 3D allowed the observation of access constraints related to the topography and to calculate the slope. The use of the multiple regression model has analyzed the relationship between the shortest accessibility distance and the socio-economic factors that are the income, gender and age. The results show that the average distance of access to the bus stop is 186m. 99.2% of the population lives within 800m from the bus stops. The results of the regression showed that income is the main factor that pushes people to take the bus and live in certain types of housing. The observation of the 3d map and the calculation of the slope made it possible to know the neighborhoods established in the hill and whose residents are susceptible to spend more energy than the other inhabitant living on flat ground. The results mean that most people living in the urban area of ​​Umeå have good access to the bus stop whatever the social group, the level of income and the type of housing.

A presente dissertação versa a teoria geral da determinação da lei aplicável no Direito Internacional Privado, sob a ótica de parâmetros cosmopolitas colhidos em outras ciências humanas, em perspectiva interdisciplinar. Analisam-se vários estudos recentes sobre o cosmopolitismo, bem como suas origens remotas, de modo a propor possíveis relações do tema com o método conflitual do Direito Internacional Privado. No capítulo I, empreende-se estudo histórico, retornando às origens do cosmopolitismo (filosofias grega e romana), passando pelo cosmopolitismo de Kant, no séc. XVIII, para então, finalmente, examinar os cosmopolitismos contemporâneos. Quanto a estes, procede-se à uma taxionomia e se exploram algumas proposições teóricas de especial utilidade para a determinação da lei aplicável. No capítulo II, busca-se transpor o cosmopolitismo do plano das demais ciências humanas para o plano da teoria jurídica em particular. Analisa-se, primeiro, o que implica a ideia de cosmopolitização do direito e, após, a relação entre o cosmopolitismo e o Direito Internacional Privado em específico. Dá-se especial atenção à aplicação da ideia de justiça cosmopolita no contexto da determinação da lei aplicável. No capítulo III, trata-se dos três grandes métodos de determinação da lei aplicável (multilateralismo, unilateralismo e materialismo), em perspectivas histórica e crítica. Conclui-se que a adoção do multilateralismo enquanto estrutura principal de determinação da lei aplicável, flexibilizado pelo princípio da proximidade, atende mais à imparcialidade cosmopolita que os outros dois métodos. No capítulo IV, propõe-se uma visão cosmopolita do princípio da ordem pública, concentrando-se em duas importantes evoluções teóricas mais recentes: as noções de ordem pública de proximidade e de ordem pública verdadeiramente internacional. Procede-se, ademais, a exame crítico da aplicação da exceção da ordem pública, no que se refere à lei aplicável, na jurisprudência brasileira recente. Ao fim do estudo, as principais conclusões são enumeradas. / This dissertation addresses international choice of law, through the lens of cosmopolitan standards collected from other humanities, undertaking an interdisciplinary approach. Many recent studies on cosmopolitanism are examined, as well as its remote origins, so as to propose links between choice of law and cosmopolitanism. In chapter I, a historical study is undertaken, going back to the origins of cosmopolitanism (Greek and Roman philosophers), dealing moreover with Kants cosmopolitanism (18th century) and contemporary cosmopolitanisms. Accordingly, a division into categories is proposed and some theoretical notions of special importance to choice of law are tackled. In chapter II, cosmopolitan concepts are applied to legal theory. First, consequences are drawn from the idea of cosmopolitanizing law. Afterwards, the relation between Conflict of Laws and cosmopolitanism is addressed. The focus is on the application of the idea of cosmopolitan justice to choice of law. In chapter III, the three existing choice of law methods are addressed (multilateralism, unilateralism and substantialism), historically and critically. It is concluded that the adoption of multilateralism as a general framework, flexibilized by the principe de proximité, is a better option in terms of cosmopolitan impartiality than the other two methods. In chapter IV, a cosmopolitan vision of public policy is proposed, focusing upon two important theories recently conceived: the notions of ordre public de proximité and of truly international public policy. Also, a critical address of the recent application of the public policy exception by Brazilian courts (within the choice of law context) is made. At the end, the main conclusions are enumerated.

The aim of my thesis is to find the shortest total route to export trash at the bus stops for several variants and also determine the suitability of approximate algorithms. The tasks are solved by insertion heuristic, closest neighbor heuristic and optimization. The optimal solution is gained by using the system LINGO and by using optimization solver CPLEX. In my thesis I first describe the problem of maintenance of bus stops. Following chapter is devoted to the role of routing problems, its classifications, problems to solve, possibilities of solution. The used methods are Traveling Salesman Problem with one or multiple vehicles available in a unique depot and Vehicle Routing Problem with one or multiple vehicles available in a unique depot or multiple depots. The last chapter describes and compare the results of the solution of insertion heuristic, closest neighbor heuristic and optimization for problem of replacement of bus schedules and of maintenance of bus stops.

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Point Cloud Registration with data measured from a photon-counting LIDAR sensor from a large distance (500 m - 1.5 km) is an expanding field. Data measuredfrom far is sparse and have low detail, which can make the registration processdifficult, and registering this type of data is fairly unexplored. In recent years,machine learning for point cloud registration has been explored with promisingresults. This work compares the performance of the point cloud registration algorithm Iterative Closest Point with state-of-the-art algorithms, with data froma photon-counting LIDAR sensor. The data was provided by the Swedish Defense Research Agency (FOI). The chosen state-of-the-art algorithms were thenon-learning-based Fast Global Registration and learning-based D3Feat and SpinNet. The results indicated that all state-of-the-art algorithms achieve a substantial increase in performance compared to the Iterative Closest Point method. Allthe state-of-the-art algorithms utilize their calculated features to obtain bettercorrespondence points and therefore, can achieve higher performance in pointcloud registration. D3Feat performed point cloud registration with the highestaccuracy of all the state-of-the-art algorithms and ICP.

A key component for automated driving is 360◦ environment detection. The recognition capabilities of mod- ern sensors are always limited to their direct field of view. In urban areas a lot of objects occlude important areas of in- terest. The information captured by another sensor from an- other perspective could solve such occluded situations. Fur- thermore, the capabilities to detect and classify various ob- jects in the surrounding can be improved by taking multiple views into account. In order to combine the data of two sensors into one co- ordinate system, a rigid transformation matrix has to be de- rived. The accuracy of modern e.g. satellite based relative pose estimation systems is not sufficient to guarantee a suit- able alignment. Therefore, a registration based approach is used in this work which aligns the captured environment data of two sensors from different positions. Thus their relative pose estimation obtained by traditional methods is improved and the data can be fused. To support this we present an approach which utilizes the uncertainty information of modern tracking systems to de- termine the possible field of view of the other sensor. Fur- thermore, it is estimated which parts of the captured data is directly visible to both, taking occlusion and shadowing ef- fects into account. Afterwards a registration method, based on the iterative closest point (ICP) algorithm, is applied to that data in order to get an accurate alignment. The contribution of the presented approch to the achiev- able accuracy is shown with the help of ground truth data from a LiDAR simulation within a 3-D crossroad model. Re- sults show that a two dimensional position and heading esti- mation is sufficient to initialize a successful 3-D registration process. Furthermore it is shown which initial spatial align- ment is necessary to obtain suitable registration results.

This thesis is about the intelligent compression of large 3D point cloud datasets. The non-parametric method that we describe simultaneously generates a continuous representation of the workspace surfaces from discrete laser samples and decimates the dataset, retaining only locally salient samples. Our framework attains decimation factors in excess of two orders of magnitude without significant degradation in fidelity. The work presented here has a specific focus on gathering and processing laser measurements taken from a moving platform in outdoor workspaces. We introduce a somewhat unusual parameterisation of the problem and look to Gaussian Processes as the fundamental machinery in our processing pipeline. Our system compresses laser data in a fashion that is naturally sympathetic to the underlying structure and complexity of the workspace. In geometrically complex areas, compression is lower than that in geometrically bland areas. We focus on this property in detail and it leads us well beyond a simple application of non-parametric techniques. Indeed, towards the end of the thesis we develop a non-stationary GP framework whereby our regression model adapts to the local workspace complexity. Throughout we construct our algorithms so that they may be efficiently implemented. In addition, we present a detailed analysis of the proposed system and investigate model parameters, metric errors and data compression rates. Finally, we note that this work is predicated on a substantial amount of robotics engineering which has allowed us to produce a high quality, peer reviewed, dataset - the first of its kind.

Simultaneous localization and mapping is the technique to construct a 3D map of unknown environment. With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this thesis, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.

Šiame darbe tiriami algoritminiai sprendimai mobiliam robotui, leidžiantys aptikti ieškomą objektą bei įvertinti jo poziciją ir orientaciją erdvėje. Atlikus šios srities technologijų analizę surasta įvairių realizacijai tinkamų metodų, tačiau bendro jų efektyvumo palyginimo trūko. Siekiant užpildyti šią spragą realizuota programinė ir techninė įranga, kuria atliktas labiausiai roboto sistemoms tinkamų metodų vertinimas. Algoritmų analizė susideda iš algoritmų tikslumo ir jų veikimo spartos vertinimo panaudojant tam paprastus bei efektyvius metodus. Darbe analizuojamas objektų orientacijos nustatymas iš Kinect kameros gylio duomenų pasitelkiant ICP algoritmą. Atliktas dviejų gylio sistemų spartos ir tikslumo tyrimas parodė, jog Kinect kamera spartos atžvilgiu yra efektyvesnis bei 2-5 kartus tikslesnis sprendimas nei įprastinė stereo kamerų sistema. Objektų aptikimo algoritmų efektyvumo eksperimentuose nustatytas maksimalus aptikimo tikslumas apie 90% bei pasiekta maksimali 15 kadrų/s veikimo sparta analizuojant standartinius VGA 640x480 raiškos vaizdus. Atliktas objektų pozicijos ir orientacijos nustatymo ICP metodo efektyvumo tyrimas parodė, jog vidutinė absoliutinė pozicijos ir orientacijos nustatymo paklaida yra atitinkamai apie 3.4cm bei apie 30 laipsnių, o veikimo sparta apie 2 kadrai/s. Tolesnis optimizavimas arba duomenų kiekio minimizavimas yra būtinas norint pasiekti geresnius veikimo rezultatus mobilioje ribotų resursų roboto sistemoje. Darbe taip pat buvo sėkmingai... [toliau žr. visą tekstą] / This work presents a performance analysis of the state-of-the-art computer vision algorithms for object detection and pose estimation. Initial field study showed that many algorithms for the given problem exist but still their combined comparison was lacking. In order to fill in the existing gap a software and hardware solution was created and the comparison of the most suitable methods for a robot system were done. The analysis consists of detector accuracy and runtime performance evaluation using simple and robust techniques. Object pose estimation via ICP algorithm and stereo vision Kinect depth sensor method was used in this work. A conducted two different stereo system analysis showed that Kinect achieves best runtime performance and its accuracy is 2-5 times more superior than a regular stereo setup. Object detection experiments showcased a maximum object detection accuracy of nearly 90% and speed of 15 fps for standard size VGA 640x480 resolution images. Accomplished object position and orientation estimation experiment using ICP method showed, that average absolute position and orientation detection error is respectively 3.4cm and 30 degrees while the runtime speed – 2 fps. Further optimization and data size minimization is necessary to achieve better efficiency on a resource limited mobile robot platform. The robot hardware system was also successfully implemented and tested in this work for object position and orientation detection.

Proximity problems and point set embeddability problems are fundamental and well-studied in computational geometry and graph drawing. Examples of such problems that are of particular interest to us in this dissertation include: finding the closest pair among a set P of points, finding the k-nearest neighbors to each point p in P, answering reverse k-nearest neighbor queries, computing the Yao graph, the Semi-Yao graph and the Euclidean minimum spanning tree of P, and mapping the vertices of a planar graph to a set P of points without inducing edge crossings. In this dissertation, we consider so-called kinetic version of these problems, that is, the points are allowed to move continuously along known trajectories, which are subject to change. We design a set of data structures and a mechanism to efficiently update the data structures. These updates occur at critical, discrete times. Also, a query may arrive at any time. We want to answer queries quickly without solving problems from scratch, so we maintain solutions continuously. We present new techniques for giving kinetic solutions with better performance for some these problems, and we provide the first kinetic results for others. In particular, we provide: • A simple kinetic data structure (KDS) to maintain all the nearest neighbors and the closest pair. Our deterministic kinetic approach for maintenance of all the nearest neighbors improves the previous randomized kinetic algorithm. • An exact KDS for maintenance of the Euclidean minimum spanning tree, which improves the previous KDS. • The first KDS's for maintenance of the Yao graph and the Semi-Yao graph. • The first KDS to consider maintaining plane graphs on moving points. • The first KDS for maintenance of all the k-nearest neighbors, for any k ≥ 1. • The first KDS to answer the reverse k-nearest neighbor queries, for any k ≥ 1 in any fixed dimension, on a set of moving points. / Graduate