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Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene
Three-dimensional (3D) reconstruction using RGB-D camera with simultaneous color image and depth information is attractive as it can significantly reduce the cost of equipment and time for data collection. Point feature is commonly used for aligning two RGB-D frames. Due to lacking reliable point fe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506666/ https://www.ncbi.nlm.nih.gov/pubmed/32887486 http://dx.doi.org/10.3390/s20174984 |
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author | Zou, Yajing Eldemiry, Amr Li, Yaxin Chen, Wu |
author_facet | Zou, Yajing Eldemiry, Amr Li, Yaxin Chen, Wu |
author_sort | Zou, Yajing |
collection | PubMed |
description | Three-dimensional (3D) reconstruction using RGB-D camera with simultaneous color image and depth information is attractive as it can significantly reduce the cost of equipment and time for data collection. Point feature is commonly used for aligning two RGB-D frames. Due to lacking reliable point features, RGB-D simultaneous localization and mapping (SLAM) is easy to fail in low textured scenes. To overcome the problem, this paper proposes a robust RGB-D SLAM system fusing both points and lines, because lines can provide robust geometry constraints when points are insufficient. To comprehensively fuse line constraints, we combine 2D and 3D line reprojection error with point reprojection error in a novel cost function. To solve the cost function and filter out wrong feature matches, we build a robust pose solver using the Gauss–Newton method and Chi-Square test. To correct the drift of camera poses, we maintain a sliding-window framework to update the keyframe poses and related features. We evaluate the proposed system on both public datasets and real-world experiments. It is demonstrated that it is comparable to or better than state-of-the-art methods in consideration with both accuracy and robustness. |
format | Online Article Text |
id | pubmed-7506666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75066662020-09-26 Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene Zou, Yajing Eldemiry, Amr Li, Yaxin Chen, Wu Sensors (Basel) Article Three-dimensional (3D) reconstruction using RGB-D camera with simultaneous color image and depth information is attractive as it can significantly reduce the cost of equipment and time for data collection. Point feature is commonly used for aligning two RGB-D frames. Due to lacking reliable point features, RGB-D simultaneous localization and mapping (SLAM) is easy to fail in low textured scenes. To overcome the problem, this paper proposes a robust RGB-D SLAM system fusing both points and lines, because lines can provide robust geometry constraints when points are insufficient. To comprehensively fuse line constraints, we combine 2D and 3D line reprojection error with point reprojection error in a novel cost function. To solve the cost function and filter out wrong feature matches, we build a robust pose solver using the Gauss–Newton method and Chi-Square test. To correct the drift of camera poses, we maintain a sliding-window framework to update the keyframe poses and related features. We evaluate the proposed system on both public datasets and real-world experiments. It is demonstrated that it is comparable to or better than state-of-the-art methods in consideration with both accuracy and robustness. MDPI 2020-09-02 /pmc/articles/PMC7506666/ /pubmed/32887486 http://dx.doi.org/10.3390/s20174984 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zou, Yajing Eldemiry, Amr Li, Yaxin Chen, Wu Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene |
title | Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene |
title_full | Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene |
title_fullStr | Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene |
title_full_unstemmed | Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene |
title_short | Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene |
title_sort | robust rgb-d slam using point and line features for low textured scene |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506666/ https://www.ncbi.nlm.nih.gov/pubmed/32887486 http://dx.doi.org/10.3390/s20174984 |
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