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FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes
Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2025.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613702517161984 |
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| access_status_str | Open Access |
| author2 | Grobler, Hans |
| author_browse | Grobler, Hans |
| author_facet | Grobler, Hans |
| collection | Thesis |
| dc_rights_str_mv | © 2024 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 (Electronic Engineering))--University of Pretoria, 2025. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/107288 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:20.984Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| 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:2263/107288 FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes Grobler, Hans u19049715@tuks.co.za Brown, Dylan UCTD Sustainable Development Goals (SDGs) Edge Grouping Pose Estimation Semi-dense Structured Features Visual Odometry Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2025. Simultaneous localisation and mapping (SLAM) is the process by which an agent, such as a robot, creates a map of the environment it traverses while simultaneously determining its position relative to the generated map. Various solutions have been proposed to solve the SLAM problem, with visual SLAM methods emerging as a prominent field of research. By using visual information, visual SLAM approaches provide feature-rich representations of the environment while primarily relying on inputs from cameras, which has enabled wide accessibility and adoption. At the heart of visual SLAM lies the visual odometry component. Visual odometry is the process by which the pose of an agent is estimated using the provided visual information. Visual odometry methods provide locally accurate pose and map estimations, while the incorporation thereof in a full SLAM system aims to make the jointly estimated poses and map globally consistent. A primary limitation of existing visual odometry approaches is their inability to achieve satisfactory performance in both high- and low-textured, and well- and low-structured regions. Existing systems only cater to a subset of the aforementioned region types. To perform accurate vision-only pose estimation in both low- and high-textured and low- and well-structured regions, a robust RGB-D visual odometry method is proposed that fuses point and edge features. By combining the descriptiveness of point features with the structure provided by edge data, a method that is robust to both low-textured and low-structured scenes is developed. This is achieved using a multi-stage pipeline. Edge features are first detected and grouped based on the Gestalt principles of similarity and proximity. Edge groups are then associated between the current and previous frames. Edge pixels are matched between the associated edge groups using the structural constraints imposed by the edges. These matches are then used to estimate the motion of the agent. The developed visual odometry method is called FPEVO. Compared to state-of-the-art alternatives, FPEVO reduces the root mean square absolute trajectory error, and translational and rotational relative pose errors, by up to 71%, 81%, and 86%, respectively. It was found that the proposed method is not only more accurate than current approaches, but also more consistent, especially in low-structured and/or low-textured environments. Although the proposed method uses an RGB-D sensor, its architecture was designed to be extendable to other sensor types, such as monocular and stereo cameras. Electrical, Electronic and Computer Engineering MEng (Electronic Engineering) Unrestricted Faculty of Engineering, Built Environment and Information Technology None 2025-12-22T08:56:54Z 2025-12-22T08:56:54Z 2026-05 2025-11-25 Dissertation * A2026 http://hdl.handle.net/2263/107288 N/A en © 2024 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 | UCTD Sustainable Development Goals (SDGs) Edge Grouping Pose Estimation Semi-dense Structured Features Visual Odometry FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes |
| title | FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes |
| title_full | FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes |
| title_fullStr | FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes |
| title_full_unstemmed | FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes |
| title_short | FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes |
| title_sort | fpevo fused point edge visual odometry for low structured and low textured scenes |
| topic | UCTD Sustainable Development Goals (SDGs) Edge Grouping Pose Estimation Semi-dense Structured Features Visual Odometry |
| url | http://hdl.handle.net/2263/107288 |