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Podeu consultar la versió en castellà a: http://hdl.handle.net/11703/10236

Methods and strategies of object localization

An important property of an intelligent robot is to be able to determine the location of an object in 3-D space. A general object localization system structure is proposed, some important issues on localization discussed, and an overview given for current available object localization algorithms and systems. The algorithms reviewed are characterized by their feature extracting and matching strategies; the range finding methods; the types of locatable objects; and the mathematical formulating methods

Anisotropic diffusion of surface normals for feature preserving surface reconstruction

technical reportFor 3D surface reconstruction problems with noisy and incomplete range data measured from complex scenes with arbitrary topologies, a low-level representation, such as level set surfaces, is used. Such surface reconstruction is typically accomplished by minimizing a weighted sum of data-model discrepancy and model smoothness terms. This paper introduces a new nonlinear model smoothness term for surface reconstruction based on variations of the surface normals. A direct solution requires solving a fourth-order partial differential equation (PDE), which is very difficult with conventional numerical techniques. Our solution is based on processing the normals separately from the surface, which allows us to separate the problem into two second-order PDEs. The proposed method can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion

Anisotropic diffusion of surface normals for feature preserving surface reconstruction

Journal ArticleFor 3D surface reconstruction problems with noisy and incomplete range data measured from complex scenes with arbitrary topologies, a low-level representation, such as level set surfaces, is used. Such surface reconstruction is typically accomplished by minimizing a weighted sum of data-model discrepancy and model smoothness terms. This paper introduces a new onlinear model smoothness term for surface reconstruction based on variations of the surface normals. A direct solution requires solving a fourth-order partial differential equation (PDE), which is very difficult with conventional numerical techniques. Our solution is based on processing the normals separately from the surface, which allows us to separate the problem into two second-order PDEs. The proposed method can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion

Anisotropic diffusion of surface normals for feature preserving surface reconstruction

Journal ArticleFor 3D surface reconstruction problems with noisy and incomplete range data measure d from complex scenes with arbitrary topologies, a low-level representation, such as level set surfaces, is used. Such surface reconstruction is typically accomplished by minimizing a weighted sum of data-model discrepancy and model smoothness terms. This paper introduces a new nonlinear model smoothness term for surface reconstruction based on variations of the surface normals. A direct solution requires solving a fourth-order partial differential equation (PDE), which is very difficult with conventional numerical techniques. Our solution is based on processing the normals separately from the surface, which allows us to separate the problem into two second-order PDEs. The proposed method can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion

A control paradigm for general purpose manipulation systems

Journal ArticleMechanical end effectors capable of dextrous manipulation are now a reality. Solutions to the high level control issues, however, have so far proved difficult to formulate. We propose a methodology for control which produces the functionality required for a general purpose manipulation system. It is clear that the state of a hand/object system is a complex interaction between the geometry of the object, the character of the contact interaction, and the conditioning of the manipulator. The objective of this work is the creation of a framework within which constraints involving the manipulator, the object, and the hand/object interaction can be exploited to direct a goal oriented manipulation strategy. The set of contacts that are applied to a task can be partitioned into subsets with independent objectives. The individual contacts may then be driven over the interaction surface to improve the state of the grasp while the configuration of the hand addresses the application of required forces. A system of this sort is flexible enough to manage large numbers of contacts and to address manipulation tasks which require the removal and replacement of fingers in the grasp. A simulator has been constructed and results of its application to position synthesis for initial grasps is presented. A discussion of the manipulation testbed under construction at the University of Utah employing the Utah/MIT Dextrous hand is presented

DETC2005-85314 AN APPROACH TO DRAWING-LIKE VIEW GENERATION FROM 3D MODELS

ABSTRACT In this paper we propose a method to generate 2D drawinglike views from 3D models automatically. The view generation process is conducted in object space and supported by two algorithms: (1) pose determination for 3D models: unifying the space between 2D drawings and 3D models; and (2) 2D drawing-like view generation from 3D models: building the correspondence between 2D drawings and 3D models. The pose determination method for 3D objects is proposed on the basis of a concept called Virtual Contact Area. Meanwhile an efficient occlusion algorithm based regular grid is described to generate orthogonal drawing-like views from 3D models along the pose orientations. To evaluate the validity of the proposed methods, respective experiments are presented. INTRODUCTION As two different ways to express and communicate design ideas, 2D drawings and 3D models are now being widely used in many fields. For designers with special skills, 2D drawings are usually used as the principal way to express ideas; while for most common users, 3D models are more intuitive than 2D drawings from the perspective of information communication. How to seamlessly transit between the two representations is a public problem for many researchers in engineering fields. In spite of the fact that many methods To find the optimal orientations for a 3D model, Park et al. [4] used a pose determination technique to integrate tw

Integrating Multiple Uncertain Views of a Static Scene Acquired by an Agile Camera System

This paper addresses the problem of merging multiple views of a static scene into a common coordinate frame, explicitly considering uncertainty. It assumes that a static world is observed by an agile vision system, whose movements are known with a limited precision, and whose observations are inaccurate and incomplete. It concentrates on acquiring uncertain three-dimensional information from multiple views, rather than on modeling or representing the information at higher levels of abstraction. Two particular problems receive attention: identifying the transformation between two viewing positions; and understanding how errors and uncertainties propagate as a result of applying the transformation. The first is solved by identifying the forward kinematics of the agile camera system. The second is solved by first treating a measurement of camera position and orientation as a uniformly distributed random vector whose component variances are related to the resolution of the encoding potentiometers, then treating an object position measurement as a normally distributed random vector whose component variances are experimentally derived, and finally determining the uncertainty of the merged points as functions of these variances

Simultaneous Localization And Modelling: SLAM for Mobile 3D Printing

Traditional additive manufacturing is constrained by the workspace of the printer, i.e. printers can only print objects within the printer's boundary. Mobile 3D printing is developed here to fabricate large-scale objects that extend beyond a printer's workspace. Mobile 3D printing uses a small-size robotic system to build large objects by connecting multiple small segments. A possible example application for this is additive construction on extraterrestrial surfaces, using locally sourced material, to minimize the overall need for equipment and materials launched from Earth. The system is equipped with both a laser total station (range and bearing sensor) and 3D scanner; measurements from these two sensors are fused to overcome the de�ciency of each individual sensor. An Extended Kalman Filter (EKF) based Simultaneous Localization And Mapping (SLAM) algorithm is implemented in order to align neighboring segments. A representation for planar patches of the model being printed, with each patch represented by 2 angles for the normal vector plus a 3D point on the patch, is proposed and shown to be particularly suited for this type of task. The system achieves sub-millimeter geometric accuracy and avoids the SLAM inconsistency problem for well beyond the bounds of odometry error that could be expected to be encountered in practice