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Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping
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_ | 1867613552452304896 |
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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:2263/40834 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:57.427Z |
| 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:2263/40834 Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping Grobler, H. deonjoub@gmail.com Joubert, Deon Robotics Vision-based SLAM Computer vision Landmark extraction Data association Landmark management Saliency Kinect 3D dataset UCTD Dissertation (MEng)--University of Pretoria, 2013. The effective application of mobile robotics requires that robots be able to perform tasks with an extended degree of autonomy. Simultaneous localisation and mapping (SLAM) aids automation by providing a robot with the means of exploring an unknown environment while being able to position itself within this environment. Vision-based SLAM benefits from the large amounts of data produced by cameras but requires intensive processing of these data to obtain useful information. In this dissertation it is proposed that, as the saliency content of an image distils a large amount of the information present, it can be used to benefit vision-based SLAM implementations. The proposal is investigated by developing a new landmark for use in SLAM. Image keypoints are grouped together according to the saliency content of an image to form the new landmark. A SLAM system utilising this new landmark is implemented in order to demonstrate the viability of using the landmark. The landmark extraction, data filtering and data association routines necessary to make use of the landmark are discussed in detail. A Microsoft Kinect is used to obtain video images as well as 3D information of a viewed scene. The system is evaluated using computer simulations and real-world datasets from indoor structured environments. The datasets used are both newly generated and freely available benchmarking ones. gm2014 Electrical, Electronic and Computer Engineering unrestricted 2014-07-17T12:14:42Z 2014-07-17T12:14:42Z 2013-04-16 2013 Dissertation Joubert, D 2013, Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/40834> E14/4/299/gm http://hdl.handle.net/2263/40834 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 | Robotics Vision-based SLAM Computer vision Landmark extraction Data association Landmark management Saliency Kinect 3D dataset UCTD Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping |
| title | Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping |
| title_full | Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping |
| title_fullStr | Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping |
| title_full_unstemmed | Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping |
| title_short | Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping |
| title_sort | saliency grouped landmarks for use in vision based simultaneous localisation and mapping |
| topic | Robotics Vision-based SLAM Computer vision Landmark extraction Data association Landmark management Saliency Kinect 3D dataset UCTD |
| url | http://hdl.handle.net/2263/40834 |