1,720,956 research outputs found
Real-time point cloud registration from RGB-D camera mounted on a robot arm using GPU acceleration
No full textRecent development of 3D scanners have provided small, precise and cheap consumer grade scanners operating in real-time. Especially because of their low weight, they are being considered for usage in visual servoing (vision aided robot control). These 3D scanners take images that are represented as an unstructured list of points represented by their position in 3D space and color called point clouds. The goal of this project has been to perform real-time point cloud registration using Graphics Processing Unit (GPU) acceleration (Registration is the task of aligning separate 3D scans into a single point cloud that minimize the distance between common features in the point clouds) for a camera mounted in an eye-in-hand configuration to perform scans of food and food-like objects. This is different from a lot of previous work that look at room scale scans.
This thesis is based on state of the art algorithms for GPU accelerated point to point based registration and extended this with a point to plane linear least squares optimizer, which is one of the novel contributions of this thesis. This also involved an implementation of a kD-tree search algorithm on the GPU. The point-to-plane optimizer is compared to a recent grid based registration algorithm that is also extended with a pre-search strategy and a multi-resolution search. In this project, a new point correspondence rejector based on a boundary rejection strategy that improves registration quality was evaluated, showing that it could greatly improve registration quality. Apart from the specific implementations, one of the main contributions of this master thesis is the use of a Relative Pose Error (RPE) based error metric to evaluate algorithms that are conceptually different.
Both performance and registration correctness has been evaluated for the GPU implementations of the 3D registration methods in this thesis, showing that real-time point to point based registration is possible for high resolution point clouds without noticeable loss of registration correctness. The GPU implementations performed are 33 times faster than similar CPU implementations and 8 times faster than the PCL-based CPU implementations. The point to plane linear least squares optimizer was proven to perform well both performance wise and correctness wise. The grid based search with its suggested improvements was observed to be fast, but didn't match the quality of the point to point based algorithms on the GPU. The algorithm correctness was evaluated using a reference trajectory generated with the robot arm. The point to point distance evaluation strategy that is typically used when evaluating registration algorithms is in this work shown to be insufficient to compare different algorithms to each other, in which the reference path strategy is an external evaluation that is shown to be an efficient metric when comparing different algorithms to each other. The 3D registration methods and implementations used in this thesis are evaluated against a number of rigid and non-rigid properties varying textural and optical properties.
This thesis is based on state of the art algorithms for GPU accelerated point to point based registration and extended this with a point to plane linear least squares optimizer (which has also involved an implementation of a kD-tree search algorithm on the GPU). This is compared to a recent grid based registration algorithm that is also extended with a pre-search strategy and a multi-resolution search. In this project, a new point correspondence rejector based on a boundary rejection strategy that improves registration quality was evaluated, showing that it could greatly improve registration quality.
Both performance and registration correctness has been evaluated for the implementations, showing that real-time point to point based registration is possible for high resolution point clouds without noticeable loss of registration correctness. The GPU implementations performed are 33 times faster than similar CPU implementations and 8 times faster than the PCL CPU implementations. The point to plane linear least squares optimizer was proven to perform well both performance wise and correctness wise. The grid based search with it's suggested improvements was observed to be fast, but didn't match the quality of the point to point based algorithms on the GPU. The algorithm correctness was evaluated using a reference trajectory generated with the robot arm. The point to point distance evaluation strategy that is typically used when evaluating registration algorithms is in this work shown to be insufficient to compare different algorithms to each other, in which the reference path strategy is an external evaluation that is shown to be an efficient metric when comparing different algorithms to each other
Real-time point cloud registration from RGB-D camera mounted on a robot arm using GPU acceleration
Full text linkRecent development of 3D scanners have provided small, precise and cheap consumer grade scanners operating in real-time. Especially because of their low weight, they are being considered for usage in visual servoing (vision aided robot control). These 3D scanners take images that are represented as an unstructured list of points represented by their position in 3D space and color called point clouds. The goal of this project has been to perform real-time point cloud registration using Graphics Processing Unit (GPU) acceleration (Registration is the task of aligning separate 3D scans into a single point cloud that minimize the distance between common features in the point clouds) for a camera mounted in an eye-in-hand configuration to perform scans of food and food-like objects. This is different from a lot of previous work that look at room scale scans.
This thesis is based on state of the art algorithms for GPU accelerated point to point based registration and extended this with a point to plane linear least squares optimizer, which is one of the novel contributions of this thesis. This also involved an implementation of a kD-tree search algorithm on the GPU. The point-to-plane optimizer is compared to a recent grid based registration algorithm that is also extended with a pre-search strategy and a multi-resolution search. In this project, a new point correspondence rejector based on a boundary rejection strategy that improves registration quality was evaluated, showing that it could greatly improve registration quality. Apart from the specific implementations, one of the main contributions of this master thesis is the use of a Relative Pose Error (RPE) based error metric to evaluate algorithms that are conceptually different.
Both performance and registration correctness has been evaluated for the GPU implementations of the 3D registration methods in this thesis, showing that real-time point to point based registration is possible for high resolution point clouds without noticeable loss of registration correctness. The GPU implementations performed are 33 times faster than similar CPU implementations and 8 times faster than the PCL-based CPU implementations. The point to plane linear least squares optimizer was proven to perform well both performance wise and correctness wise. The grid based search with its suggested improvements was observed to be fast, but didn't match the quality of the point to point based algorithms on the GPU. The algorithm correctness was evaluated using a reference trajectory generated with the robot arm. The point to point distance evaluation strategy that is typically used when evaluating registration algorithms is in this work shown to be insufficient to compare different algorithms to each other, in which the reference path strategy is an external evaluation that is shown to be an efficient metric when comparing different algorithms to each other. The 3D registration methods and implementations used in this thesis are evaluated against a number of rigid and non-rigid properties varying textural and optical properties.
This thesis is based on state of the art algorithms for GPU accelerated point to point based registration and extended this with a point to plane linear least squares optimizer (which has also involved an implementation of a kD-tree search algorithm on the GPU). This is compared to a recent grid based registration algorithm that is also extended with a pre-search strategy and a multi-resolution search. In this project, a new point correspondence rejector based on a boundary rejection strategy that improves registration quality was evaluated, showing that it could greatly improve registration quality.
Both performance and registration correctness has been evaluated for the implementations, showing that real-time point to point based registration is possible for high resolution point clouds without noticeable loss of registration correctness. The GPU implementations performed are 33 times faster than similar CPU implementations and 8 times faster than the PCL CPU implementations. The point to plane linear least squares optimizer was proven to perform well both performance wise and correctness wise. The grid based search with it's suggested improvements was observed to be fast, but didn't match the quality of the point to point based algorithms on the GPU. The algorithm correctness was evaluated using a reference trajectory generated with the robot arm. The point to point distance evaluation strategy that is typically used when evaluating registration algorithms is in this work shown to be insufficient to compare different algorithms to each other, in which the reference path strategy is an external evaluation that is shown to be an efficient metric when comparing different algorithms to each other
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Fast and accurate GPU accelerated, high resolution 3D registration for robotic 3D reconstruction of compliant Food objects
Full text linkIf we are to develop robust robot-based automation in primary production and processing in the agriculture and ocean space sectors, we have to develop solid vision-based perception for the robots. Accurate vision-based perception requires fast 3D reconstruction of the object in order to extract the geometrical features necessary for robotic manipulation. To this end, we present an accurate, real-time and high-resolution ICP-based 3D registration algorithm for eye-in-hand configuration using an RBG-D camera. Our 3D reconstruction, via an efficient GPU implementation, is up to 33 times faster than a similar CPU implementation, and up to eight times faster than a similar library implementation, resulting in point clouds of 1 mm resolution. The comparison of our 3D reconstruction with other ICP-based baselines, through trajectories from 3D registration and reference trajectories for an eye-in-hand configuration, shows that the point-to-plane linear least squares optimizer gives the best results, both in terms of precision and performance. Our method is validated for the eye-in-hand robotic scanning and 3D reconstruction of some representative examples of food items and produce of agricultural and marine origin.publishedVersion© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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