A Survey of Procedural Techniques for City Generation

The computer game industry requires a skilled workforce and this combined with the complexity of modern games, means that production costs are extremely high. One of the most time consuming aspects is the creation of game geometry, the virtual world which the players inhabit. Procedural techniques have been used within computer graphics to create natural textures, simulate special effects and generate complex natural models including trees and waterfalls. It is these procedural techniques that we intend to harness to generate geometry and textures suitable for a game situated in an urban environment. Procedural techniques can provide many benefits for computer graphics applications when the correct algorithm is used. An overview of several commonly used procedural techniques including fractals, L-systems, Perlin noise, tiling systems and cellular basis is provided. The function of each technique and the resulting output they create are discussed to better understand their characteristics, benefits and relevance to the city generation problem. City generation is the creation of an urban area which necessitates the creation of buildings, situated along streets and arranged in appropriate patterns. Some research has already taken place into recreating road network patterns and generating buildings that can vary in function and architectural style. We will study the main body of existing research into procedural city generation and provide an overview of their implementations and a critique of their functionality and results. Finally we present areas in which further research into the generation of cities is required and outline our research goals for city generation

Intelligent manipulation technique for multi-branch robotic systems

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system

Perception for detection and grasping

The final publication is available at link.springer.comThis research presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in earlier chapters. The methods can run in real-time.Peer ReviewedPostprint (author's final draft

Space colonisation based procedural road generation

Dissertação de mestrado em Computer ScienceProcedural generation of content has been studied for quite some time and it is increasingly relevant in scientific areas and in video-game and film industries. Procedural road layout generation has been traditionally approached using L-Systems, with some works exploring alternative avenues. Although originally conceived for biological systems modelling, the adequacy of L-Systems as a base for road generation has been demonstrated in several works. In this context, this work presents an alternative approach for procedural road layout generation that is also inspired by plant generation algorithms: space colonisation. In particular, this work uses the concept of attraction points introduced in space colonisation as its base to produce road layouts, both in urban and inter-city environments. As will be shown, the usage of attraction points provides an intuitive way to parameterise a road layout. The original Space Colonization Algorithm (SCA) generates a tree like structure, but in this work, the extensions made aim to fully generate a inter-connected road network. As most previous methods the method has two phases. A first phase generates what is mostly a tree structure growing from user defined road segments. The second phase performs the inter connectivity among the roads created in the first phase. The original SCA parameters such as the killradius help to control the capillarity of the road layout, the number of attraction points used by each segment will dictate its relevance establishing a road hierarchy naturally dependent on the distribution of the attraction points on the terrain. An angle control allows the creation of grid like or more organic road layouts. The distribution of the attraction points in the terrain can be conditioned by boundary maps, containing parks, sea, rivers, and other forbidden areas. Population density maps can be used to supply an explicit probabilistic distribution to the attraction points. Flow-fields can be used to dictate the flow of the road layout. Elevation maps provide an additional restriction regarding the steepness of the roads. The tests were executed within a graphic toolbox developed simultaneously. The results are exported to a geographical information file format, GeoJSON, and then maps are rendered using a geospatial visualisation and processing framework called Mapnik. For the most part, parameter settings were intuitively reflected on the road layout and this method can be seen as a first step towards fully exploring the usage of attraction points in the context of road layout.Gradualmente a geração procedimental de conteúdo tem-se tornado cada vez mais relevante, sendo maioritariamente aplicada em industrias como a dos vídeo-jogos e cinema. No que toca à geração procedimental de redes de estradas, grande parte das abordagens em torno deste tema são baseadas em L-Systems. Embora a área de aplicação dos L-Systems tenha sido originalmente para produzir modelos de sistemas biológicos, mostrou também ser um algoritmo adequado para a geração procedimental de redes de estradas. Este trabalho apresenta uma abordagem alternativa à geração procedimental de redes de estradas que também é inspirada num algoritmo procedimental de geração de plantas, colonização espacial, utilizando o conceito de pontos de atracão como base para gerar padrões de estradas. Como será demonstrado, a utilização de pontos de atracão fornece uma maneira intuitiva de parametrizar um padrão de estradas desejado. Como a maioria dos trabalhos feitos nesta área, este método tem duas fases. A primeira fase gera uma rede semelhante a uma árvore criada a partir de um ou mais segmentos iniciais da rede determinados pelo utilizador. A segunda fase trata de interligar as estradas geradas na primeira fase. Os parâmetros iniciais do algoritmo de colonização espacial, como o kill radius, ajudam a controlar a capilaridade da rede, os pontos de atracão que influenciam cada segmento irão ditar a sua relevância na rede geral, estabelecendo a noção de hierarquia de estradas, dependendo da distribuição de pontos de atracão no terreno. O controlo do ângulo entre segmentos permite a criação de padrões de estradas tanto em forma de grelha como padrões mais orgânicos. A distribuição dos pontos de atracão no terreno pode ser influenciada por mapas de fronteira, que contem as áreas válidas e/ou inválidas, como parques, mar, rios, e outras áreas proibidas. Mapas de densidade populacional podem ser usados para fornecer uma distribuição probabilística dos pontos de atracão. Campos de forças, podem ser usados para ditar o fluxo da rede de estradas. Mapas de elevação oferecem uma restrição adicional tendo em conta a inclinação das estradas. De um modo geral, as definições de parâmetros refletiram-se de um modo intuitivo nos padrões de redes de estradas gerados, e este trabalho pode ser considerado como um primeiro passo na exploração do conceito de pontos de atracão na área da geração de redes de estradas

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Generative Street Addresses from Satellite Imagery

We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions. Keywords: road extraction; remote sensing; satellite imagery; machine learning; supervised learning; generative schemes; automatic geocodin