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Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments
Dissertation (MEng)--University of Pretoria, 2013.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2014
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| _version_ | 1867613565005856768 |
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| access_status_str | Open Access |
| author2 | Grobler, H. |
| author_browse | Grobler, H. |
| author_facet | Grobler, H. |
| collection | Thesis |
| dc_rights_str_mv | © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng)--University of Pretoria, 2013. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/33323 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:09.710Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/33323 Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments Grobler, H. Van Wyk, Frans-Pieter Object recognition Pose estimation Real-time Partial object matching 3D features Free form deformation Data compression Locality sensitive hashing Structured light Intelligent systems UCTD Dissertation (MEng)--University of Pretoria, 2013. Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into environments previously thought to be too perilous for humans to operate in. Some robots can be used to extract potentially hazardous materials from sites inaccessible to humans, while others are being developed to aid humans with laborious tasks. A crucial aspect of all artificial systems is the manner in which they interact with their immediate surroundings. Developing such a deceivingly simply aspect has proven to be significantly challenging, as it not only entails the methods through which the system perceives its environment, but also its ability to perform critical tasks. These undertakings often involve the coordination of numerous subsystems, each performing its own complex duty. To complicate matters further, it is nowadays becoming increasingly important for these artificial systems to be able to perform their tasks in real-time. The task of object recognition is typically described as the process of retrieving the object in a database that is most similar to an unknown, or query, object. Pose estimation, on the other hand, involves estimating the position and orientation of an object in three-dimensional space, as seen from an observer’s viewpoint. These two tasks are regarded as vital to many computer vision techniques and regularly serve as input to more complex perception algorithms. An approach is presented which regards the object recognition and pose estimation procedures as mutually dependent. The core idea is that dissimilar objects might appear similar when observed from certain viewpoints. A feature-based conceptualisation, which makes use of a database, is implemented and used to perform simultaneous object recognition and pose estimation. The design incorporates data compression techniques, originally suggested by the image-processing community, to facilitate fast processing of large databases. System performance is quantified primarily on object recognition, pose estimation and execution time characteristics. These aspects are investigated under ideal conditions by exploiting three-dimensional models of relevant objects. The performance of the system is also analysed for practical scenarios by acquiring input data from a structured light implementation, which resembles that obtained from many commercial range scanners. Practical experiments indicate that the system was capable of performing simultaneous object recognition and pose estimation in approximately 230 ms once a novel object has been sensed. An average object recognition accuracy of approximately 73% was achieved. The pose estimation results were reasonable but prompted further research. The results are comparable to what has been achieved using other suggested approaches such as Viewpoint Feature Histograms and Spin Images. gm2014 Electrical, Electronic and Computer Engineering unrestricted 2014-02-11T05:09:38Z 2014-02-11T05:09:38Z 2013-09-04 2013 Dissertation Van Wyk, FP 2013, Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/33323> E13/9/1010/gm http://hdl.handle.net/2263/33323 en © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Object recognition Pose estimation Real-time Partial object matching 3D features Free form deformation Data compression Locality sensitive hashing Structured light Intelligent systems UCTD Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments |
| title | Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments |
| title_full | Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments |
| title_fullStr | Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments |
| title_full_unstemmed | Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments |
| title_short | Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments |
| title_sort | simutaneous real time object recognition and pose estimation for artificial systems operating in dynamic environments |
| topic | Object recognition Pose estimation Real-time Partial object matching 3D features Free form deformation Data compression Locality sensitive hashing Structured light Intelligent systems UCTD |
| url | http://hdl.handle.net/2263/33323 |