Navigace mobilních robotů v neznámém prostředí s využitím měření vzdáleností

The ability of a robot to navigate itself in the environment is a crucial step towards its autonomy. Navigation as a subtask of the development of autonomous robots is the subject of this thesis, focusing on the development of a method for simultaneous localization an mapping (SLAM) of mobile robots in six degrees of freedom (DOF). As a part of this research, a platform for 3D range data acquisition based on a continuously inclined laser rangefinder was developed. This platform is presented, evaluating the measurements and also presenting the robotic equipment on which the platform can be fitted. The localization and mapping task is equal to the registration of multiple 3D images into a common frame of reference. For this purpose, a method based on the Iterative Closest Point (ICP) algorithm was developed. First, the originally implemented SLAM method is presented, focusing on the time-wise performance and the registration quality issues introduced by the implemented algorithms. In order to accelerate and improve the quality of the time-demanding 6DOF image registration, an extended method was developed. The major extension is the introduction of a factorized registration, extracting 2D representations of vertical objects called leveled maps from the 3D point sets, ensuring these representations are 3DOF invariant. The extracted representations are registered in 3DOF using ICP algorithm, allowing pre-alignment of the 3D data for the subsequent robust 6DOF ICP based registration. The extended method is presented, showing all important modifications to the original method. The developed registration method was evaluated using real 3D data acquired in different indoor environments, examining the benefits of the factorization and other extensions as well as the performance of the original ICP based method. The factorization gives promising results compared to a single phase 6DOF registration in vertically structured environments. Also, the disadvantages of the method are discussed, proposing possible solutions. Finally, the future prospects of the research are presented.Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.

Composing quadrilateral meshes for animation

The modeling-by-composition paradigm can be a powerful tool in modern animation pipelines. We propose two novel interactive techniques to compose 3D assets that enable the artists to freely remove, detach and combine components of organic models. The idea behind our methods is to preserve most of the original information in the input characters and blend accordingly where necessary. The first method, QuadMixer, provides a robust tool to compose the quad layouts of watertight pure quadrilateral meshes, exploiting the boolean operations defined on triangles. Quad Layout is a crucial property for many applications since it conveys important information that would otherwise be destroyed by techniques that aim only at preserving the shape. Our technique keeps untouched all the quads in the patches which are not involved in the blending. The resulting meshes preserve the originally designed edge flows that, by construction, are captured and incorporated into the new quads. SkinMixer extends this approach to compose skinned models, taking into account not only the surface but also the data structures for animating the character. We propose a new operation-based technique that preserves and smoothly merges meshes, skeletons, and skinning weights. The retopology approach of QuadMixer is extended to work on quad-dominant and arbitrary complex surfaces. Instead of relying on boolean operations on triangle meshes, we manipulate signed distance fields to generate an implicit surface. The results preserve most of the information in the input assets, blending accordingly in the intersection regions. The resulting characters are ready to be used in animation pipelines. Given the high quality of the results generated, we believe that our methods could have a huge impact on the entertainment industry. Integrated into current software for 3D modeling, they would certainly provide a powerful tool for the artists. Allowing them to automatically reuse parts of their well-designed characters could lead to a new approach for creating models, which would significantly reduce the cost of the process

Learning quadrangulated patches for 3D shape parameterization and completion

We propose a novel 3D shape parameterization by surface patches, that are oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the shape. Unlike previous methods, we are able to encode surface patches of variable size as determined by the user. We propose novel methods for dictionary learning and patch reconstruction based on the query of a noisy input patch with holes. We evaluate the patch dictionary towards various applications in 3D shape inpainting, denoising and compression. Our method is able to predict missing vertices and inpaint moderately sized holes. We demonstrate a complete pipeline for reconstructing the 3D mesh from the patch encoding. We validate our shape parameterization and reconstruction methods on both synthetic shapes and real world scans. We show that our patch dictionary performs successful shape completion of complicated surface textures.Comment: To be presented at International Conference on 3D Vision 2017, 201

Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation

Mesh generation is a fundamental and critical problem in geometric data modeling and processing. In most scientific and engineering tasks that involve numerical computations and simulations on 2D/3D regions or on curved geometric objects, discretizing or approximating the geometric data using a polygonal or polyhedral meshes is always the first step of the procedure. The quality of this tessellation often dictates the subsequent computation accuracy, efficiency, and numerical stability. When compared with unstructured meshes, the structured meshes are favored in many scientific/engineering tasks due to their good properties. However, generating high-quality structured mesh remains challenging, especially for complex or large-scale geometric data. In industrial Computer-aided Design/Engineering (CAD/CAE) pipelines, the geometry processing to create a desirable structural mesh of the complex model is the most costly step. This step is semi-manual, and often takes up to several weeks to finish. Several technical challenges remains unsolved in existing structured mesh generation techniques. This dissertation studies the effective generation of structural mesh on large and complex geometric data. We study a general geometric computation paradigm to solve this problem via model partitioning and divide-and-conquer. To apply effective divide-and-conquer, we study two key technical components: the shape decomposition in the divide stage, and the structured meshing in the conquer stage. We test our algorithm on vairous data set, the results demonstrate the efficiency and effectiveness of our framework. The comparisons also show our algorithm outperforms existing partitioning methods in final meshing quality. We also show our pipeline scales up efficiently on HPC environment

Semantic 3D Reconstruction with Finite Element Bases

We propose a novel framework for the discretisation of multi-label problems on arbitrary, continuous domains. Our work bridges the gap between general FEM discretisations, and labeling problems that arise in a variety of computer vision tasks, including for instance those derived from the generalised Potts model. Starting from the popular formulation of labeling as a convex relaxation by functional lifting, we show that FEM discretisation is valid for the most general case, where the regulariser is anisotropic and non-metric. While our findings are generic and applicable to different vision problems, we demonstrate their practical implementation in the context of semantic 3D reconstruction, where such regularisers have proved particularly beneficial. The proposed FEM approach leads to a smaller memory footprint as well as faster computation, and it constitutes a very simple way to enable variable, adaptive resolution within the same model

A GROWTH-BASED APPROACH TO THE AUTOMATIC GENERATION OF NAVIGATION MESHES

Providing an understanding of space in game and simulation environments is one of the major challenges associated with moving artificially intelligent characters through these environments. The usage of some form of navigation mesh has become the standard method to provide a representation of the walkable space in game environments to characters moving around in that environment. There is currently no standardized best method of producing a navigation mesh. In fact, producing an optimal navigation mesh has been shown to be an NP-Hard problem. Current approaches are a patchwork of divergent methods all of which have issues either in the time to create the navigation meshes (e.g., the best looking navigation meshes have traditionally been produced by hand which is time consuming), generate substandard quality navigation meshes (e.g., many of the automatic mesh production algorithms result in highly triangulated meshes that pose problems for character navigation), or yield meshes that contain gaps of areas that should be included in the mesh and are not (e.g., existing growth-based methods are unable to adapt to non-axis-aligned geometry and as such tend to provide a poor representation of the walkable space in complex environments). We introduce the Planar Adaptive Space Filling Volumes (PASFV) algorithm, Volumetric Adaptive Space Filling Volumes (VASFV) algorithm, and the Iterative Wavefront Edge Expansion Cell Decomposition (Wavefront) algorithm. These algorithms provide growth-based spatial decompositions for navigation mesh generation in either 2D (PASFV) or 3D (VASFV). These algorithms generate quick (on demand) decompositions (Wavefront), use quad/cube base spatial structures to provide more regular regions in the navigation mesh instead of triangles, and offer full coverage decompositions to avoid gaps in the navigation mesh by adapting to non-axis-aligned geometry. We have shown experimentally that the decompositions offered by PASFV and VASFV are superior both in character navigation ability, number of regions, and coverage in comparison to the existing and commonly used techniques of Space Filling Volumes, Hertel-Melhorn decomposition, Delaunay Triangulation, and Automatic Path Node Generation. Finally, we show that our Wavefront algorithm retains the superior performance of the PASFV and VASFV algorithms while providing faster decompositions that contain fewer degenerate and near degenerate regions. Unlike traditional navigation mesh generation techniques, the PASFV and VASFV algorithms have a real time extension (Dynamic Adaptive Space Filling Volumes, DASFV) which allows the navigation mesh to adapt to changes in the geometry of the environment at runtime. In addition, it is possible to use a navigation mesh for applications above and beyond character path planning and navigation. These multiple uses help to increase the return on the investment in creating a navigation mesh for a game or simulation environment. In particular, we will show how to use a navigation mesh for the acceleration of collision detection