Extracting surveillance graphs from robot maps

Abstract — GRAPH-CLEAR is a recently introduced theo-retical framework to model surveillance tasks accomplished by multiple robots patrolling complex indoor environments. In this paper we provide a first step to close the loop between its graph-based theoretical formulation and practical scenarios. We show how it is possible to algorithmically extract suitable so-called surveillance graphs from occupancy grid maps. We also identify local graph modification operators, called contractions, that alter the graph being extracted so that the original surveillance problem can be solved using less robots. The algorithm we present is based on the Generalized Voronoi Diagram, a structure that can be simply computed using watershed like algorithms. Our algorithm is evaluated by processing maps produced by mobile robots exploring indoor environments. It turns out that the proposed algorithm is fast, robust to noise, and opportunistically modifies the graph so that less expensive strategies can be computed. I

Surface Reconstruction from Unorganized Point Cloud Data via Progressive Local Mesh Matching

This thesis presents an integrated triangle mesh processing framework for surface reconstruction based on Delaunay triangulation. It features an innovative multi-level inheritance priority queuing mechanism for seeking and updating the optimum local manifold mesh at each data point. The proposed algorithms aim at generating a watertight triangle mesh interpolating all the input points data when all the fully matched local manifold meshes (umbrellas) are found. Compared to existing reconstruction algorithms, the proposed algorithms can automatically reconstruct watertight interpolation triangle mesh without additional hole-filling or manifold post-processing. The resulting surface can effectively recover the sharp features in the scanned physical object and capture their correct topology and geometric shapes reliably. The main Umbrella Facet Matching (UFM) algorithm and its two extended algorithms are documented in detail in the thesis. The UFM algorithm accomplishes and implements the core surface reconstruction framework based on a multi-level inheritance priority queuing mechanism according to the progressive matching results of local meshes. The first extended algorithm presents a new normal vector combinatorial estimation method for point cloud data depending on local mesh matching results, which is benefit to sharp features reconstruction. The second extended algorithm addresses the sharp-feature preservation issue in surface reconstruction by the proposed normal vector cone (NVC) filtering. The effectiveness of these algorithms has been demonstrated using both simulated and real-world point cloud data sets. For each algorithm, multiple case studies are performed and analyzed to validate its performance

Efficient mobile robot path planning by Voronoi-based heuristic

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Doctor of Philosophy

dissertationShape analysis is a well-established tool for processing surfaces. It is often a first step in performing tasks such as segmentation, symmetry detection, and finding correspondences between shapes. Shape analysis is traditionally employed on well-sampled surfaces where the geometry and topology is precisely known. When the form of the surface is that of a point cloud containing nonuniform sampling, noise, and incomplete measurements, traditional shape analysis methods perform poorly. Although one may first perform reconstruction on such a point cloud prior to performing shape analysis, if the geometry and topology is far from the true surface, then this can have an adverse impact on the subsequent analysis. Furthermore, for triangulated surfaces containing noise, thin sheets, and poorly shaped triangles, existing shape analysis methods can be highly unstable. This thesis explores methods of shape analysis applied directly to such defect-laden shapes. We first study the problem of surface reconstruction, in order to obtain a better understanding of the types of point clouds for which reconstruction methods contain difficulties. To this end, we have devised a benchmark for surface reconstruction, establishing a standard for measuring error in reconstruction. We then develop a new method for consistently orienting normals of such challenging point clouds by using a collection of harmonic functions, intrinsically defined on the point cloud. Next, we develop a new shape analysis tool which is tolerant to imperfections, by constructing distances directly on the point cloud defined as the likelihood of two points belonging to a mutually common medial ball, and apply this for segmentation and reconstruction. We extend this distance measure to define a diffusion process on the point cloud, tolerant to missing data, which is used for the purposes of matching incomplete shapes undergoing a nonrigid deformation. Lastly, we have developed an intrinsic method for multiresolution remeshing of a poor-quality triangulated surface via spectral bisection

Machining accuracy enhancement using machine tool error compensation and metrology

This dissertation aims to enhance machining accuracy by machine tool error reduction and workpiece metrology. The error characteristics are studied by building a quasi-static error model. Perturbed forward kinematic model is used for modeling a 5-axis Computer Numerical Control (CNC) machine with one redundant linear axis. It is found that the 1st order volumetric error model of the 5-axis machine is attributed to 32 error parameter groups. To identify the model by estimating these parameter groups using the least-squares fitting, errors at 290 quasi-randomly generated measurement points over the machine’s workspace are measured using a laser tracker. The identified error model explains 90% of the mean error of the training data sets. However, the measurements using the laser tracker take about 90 minutes, which may cause the identified error parameters to be inaccurate due to the slow varying and transient natures of thermal errors. To shorten the measurement time, an experimental design approach, which suggests the optimal observation locations such that the corresponding robustness of identification is maximized, is applied to design the optimal error observers. Since the observers must be uniformly distributed over the workspace for gaining redundancy, the constrained K-optimal designs are used to select 80 K-optimal observers for the 5-axis machine. Six measurement cycles using 80 observers are done at machine’s different thermal states within a 400-minute experiment. Six error models are trained with consistent performances and are found to be comparable to the one trained by 290 quasi-random observations. This shows the feasibility of using smaller but more strategical-chosen point-set in data-driven error models. More importantly, the growth on mean nominal (119.1 to 181.9 microns) and modeled error (26.3 to 33.9 microns) suggest the necessity of thermal error tracking for enhancing the machining accuracy. A point-set based metrology is also developed to compensate the inaccuracies introduced by workpiece and fixtures and enhance machining accuracy. The machinability of all planar features is examined by virtually comparing the scanned data with the nominal machining planes, which are also known as virtual gages. The virtual gaging problem is modeled as a constrained linear program. The optimal solution to the problem can compensate the displacement introduced by workpiece and fixtures and hence guarantee a conforming finished part. To transfer point-set data into mathematical constraints, algorithms that align, segment, downsize and filter the point-set data are exploited. The concept of virtual gage analysis is demonstrated using experimental data for a simple raw casting. However, for the case where the casting is defective, and some virtual gages are not feasible, the corresponding linear program was found to have no solution. By introducing slack variables to the original linear programming problem, the extended problem has been solved. The extended model is validated for the data obtained for another casting. Further, the analysis predicts the machining allowances on all functional features. Cylindrical surface and its tolerance verification play important role in machining process. Although there exist many approaches that can fit the maximum, minimum and minimum zone cylinders, the cylinder fitting problems can be even simplified. The proposed methodology seeks to reduce the number of parameters used in cylinder fitting model by using the projection model that considers the degenerated tolerance specifications of the projected 2-D point-set. Also, to avoid the problem of local optimum by introducing the optimal direction of projection such that the 2-D point projected onto this direction has optimal tolerance specifications (maximum, minimum and minimum zone circles), global optimum solver such as Particle Swarm Optimization (PSO) is used. The proposed simplified method shows consistent results compared with the results from literature

Reconstructing plant architecture from 3D laser scanner data

En infographie, les modèles virtuels de plantes sont de plus en plus réalistes visuellement. Cependant, dans le contexte de la biologie et l'agronomie, l'acquisition de modèles précis de plantes réelles reste un problème majeur pour la construction de modèles quantitatifs du développement des plantes. Récemment, des scanners laser 3D permettent d'acquérir des images 3D avec pour chaque pixel une profondeur correspondant à la distance entre le scanner et la surface de l'objet visé. Cependant, une plante est généralement un ensemble important de petites surfaces sur lesquelles les méthodes classiques de reconstruction échouent. Dans cette thèse, nous présentons une méthode pour reconstruire des modèles virtuels de plantes à partir de scans laser. Mesurer des plantes avec un scanner laser produit des données avec différents niveaux de précision. Les scans sont généralement denses sur la surface des branches principales mais recouvrent avec peu de points les branches fines. Le cur de notre méthode est de créer itérativement un squelette de la structure de la plante en fonction de la densité locale de points. Pour cela, une méthode localement adaptative a été développée qui combine une phase de contraction et un algorithme de suivi de points. Nous présentons également une procédure d'évaluation quantitative pour comparer nos reconstructions avec des structures reconstruites par des experts de plantes réelles. Pour cela, nous explorons d'abord l'utilisation d'une distance d'édition entre arborescence. Finalement, nous formalisons la comparaison sous forme d'un problème d'assignation pour trouver le meilleur appariement entre deux structures et quantifier leurs différences. (Résumé d'auteur

Estimating Anthropometric Marker Locations from 3-D LADAR Point Clouds

An area of interest for improving the identification portion of the system is in extracting anthropometric markers from a Laser Detection and Ranging (LADAR) point cloud. Analyzing anthropometrics markers is a common means of studying how a human moves and has been shown to provide good results in determining certain demographic information about the subject. This research examines a marker extraction method utilizing principal component analysis (PCA), self-organizing maps (SOM), alpha hulls, and basic anthropometric knowledge. The performance of the extraction algorithm is tested by performing gender classification with the calculated markers

A Survey of Surface Reconstruction from Point Clouds

International audienceThe area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations – not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques, and provide directions for future work in surface reconstruction