Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance
Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panel...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tao, Yong [verfasserIn] |
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E-Artikel |
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Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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© The Author(s) 2023 |
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Enthalten in: Chinese Journal of Mechanical Engineering - Chinese Mechanical Engineering Society, 2012, 36(2023), 1 vom: 27. Juni |
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| Übergeordnetes Werk: |
volume:36 ; year:2023 ; number:1 ; day:27 ; month:06 |
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10.1186/s10033-023-00902-9 |
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SPR052062899 |
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| 520 | |a Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. | ||
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| 700 | 1 | |a Duan, Lian |4 aut | |
| 700 | 1 | |a Lan, Jiangbo |4 aut | |
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10.1186/s10033-023-00902-9 doi (DE-627)SPR052062899 (SPR)s10033-023-00902-9-e DE-627 ger DE-627 rakwb eng Tao, Yong verfasserin (orcid)0000-0002-8585-0797 aut Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. Autonomous mobile robot (dpeaa)DE-He213 Multi-sensor fusion (dpeaa)DE-He213 Glass recognition (dpeaa)DE-He213 Map optimization (dpeaa)DE-He213 Gao, He aut Wen, Yufang aut Duan, Lian aut Lan, Jiangbo aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 36(2023), 1 vom: 27. Juni (DE-627)SPR008124000 nnns volume:36 year:2023 number:1 day:27 month:06 https://dx.doi.org/10.1186/s10033-023-00902-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 36 2023 1 27 06 |
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10.1186/s10033-023-00902-9 doi (DE-627)SPR052062899 (SPR)s10033-023-00902-9-e DE-627 ger DE-627 rakwb eng Tao, Yong verfasserin (orcid)0000-0002-8585-0797 aut Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. Autonomous mobile robot (dpeaa)DE-He213 Multi-sensor fusion (dpeaa)DE-He213 Glass recognition (dpeaa)DE-He213 Map optimization (dpeaa)DE-He213 Gao, He aut Wen, Yufang aut Duan, Lian aut Lan, Jiangbo aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 36(2023), 1 vom: 27. Juni (DE-627)SPR008124000 nnns volume:36 year:2023 number:1 day:27 month:06 https://dx.doi.org/10.1186/s10033-023-00902-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 36 2023 1 27 06 |
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10.1186/s10033-023-00902-9 doi (DE-627)SPR052062899 (SPR)s10033-023-00902-9-e DE-627 ger DE-627 rakwb eng Tao, Yong verfasserin (orcid)0000-0002-8585-0797 aut Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. Autonomous mobile robot (dpeaa)DE-He213 Multi-sensor fusion (dpeaa)DE-He213 Glass recognition (dpeaa)DE-He213 Map optimization (dpeaa)DE-He213 Gao, He aut Wen, Yufang aut Duan, Lian aut Lan, Jiangbo aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 36(2023), 1 vom: 27. Juni (DE-627)SPR008124000 nnns volume:36 year:2023 number:1 day:27 month:06 https://dx.doi.org/10.1186/s10033-023-00902-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 36 2023 1 27 06 |
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10.1186/s10033-023-00902-9 doi (DE-627)SPR052062899 (SPR)s10033-023-00902-9-e DE-627 ger DE-627 rakwb eng Tao, Yong verfasserin (orcid)0000-0002-8585-0797 aut Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. Autonomous mobile robot (dpeaa)DE-He213 Multi-sensor fusion (dpeaa)DE-He213 Glass recognition (dpeaa)DE-He213 Map optimization (dpeaa)DE-He213 Gao, He aut Wen, Yufang aut Duan, Lian aut Lan, Jiangbo aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 36(2023), 1 vom: 27. Juni (DE-627)SPR008124000 nnns volume:36 year:2023 number:1 day:27 month:06 https://dx.doi.org/10.1186/s10033-023-00902-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 36 2023 1 27 06 |
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10.1186/s10033-023-00902-9 doi (DE-627)SPR052062899 (SPR)s10033-023-00902-9-e DE-627 ger DE-627 rakwb eng Tao, Yong verfasserin (orcid)0000-0002-8585-0797 aut Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. Autonomous mobile robot (dpeaa)DE-He213 Multi-sensor fusion (dpeaa)DE-He213 Glass recognition (dpeaa)DE-He213 Map optimization (dpeaa)DE-He213 Gao, He aut Wen, Yufang aut Duan, Lian aut Lan, Jiangbo aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 36(2023), 1 vom: 27. Juni (DE-627)SPR008124000 nnns volume:36 year:2023 number:1 day:27 month:06 https://dx.doi.org/10.1186/s10033-023-00902-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 36 2023 1 27 06 |
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Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. © The Author(s) 2023 |
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Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. © The Author(s) 2023 |
| abstract_unstemmed |
Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions. © The Author(s) 2023 |
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Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance |
| url |
https://dx.doi.org/10.1186/s10033-023-00902-9 |
| remote_bool |
true |
| author2 |
Gao, He Wen, Yufang Duan, Lian Lan, Jiangbo |
| author2Str |
Gao, He Wen, Yufang Duan, Lian Lan, Jiangbo |
| ppnlink |
SPR008124000 |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.1186/s10033-023-00902-9 |
| up_date |
2024-07-04T01:04:55.363Z |
| _version_ |
1803608491678498816 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR052062899</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230628064807.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230628s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s10033-023-00902-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR052062899</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10033-023-00902-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tao, Yong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-8585-0797</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. 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7.4022045 |