Object-aware data association for the semantically constrained visual SLAM

Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for...
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Autor*in:	Liu, Yang [verfasserIn] Guo, Chi Wang, Yingli

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Visual SLAM Robotic Semantic SLAM Computer vision

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Intelligent service robotics - Berlin : Springer, 2008, 16(2023), 2 vom: 15. Feb., Seite 155-176
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:2 ; day:15 ; month:02 ; pages:155-176

Links:	Volltext

DOI / URN:	10.1007/s11370-023-00455-9

Katalog-ID:	SPR050003844

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520			\|a Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available.
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allfields	10.1007/s11370-023-00455-9 doi (DE-627)SPR050003844 (SPR)s11370-023-00455-9-e DE-627 ger DE-627 rakwb eng Liu, Yang verfasserin aut Object-aware data association for the semantically constrained visual SLAM 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. Visual SLAM (dpeaa)DE-He213 Robotic (dpeaa)DE-He213 Semantic SLAM (dpeaa)DE-He213 Computer vision (dpeaa)DE-He213 Guo, Chi aut Wang, Yingli aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 16(2023), 2 vom: 15. Feb., Seite 155-176 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:16 year:2023 number:2 day:15 month:02 pages:155-176 https://dx.doi.org/10.1007/s11370-023-00455-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 2 15 02 155-176
spelling	10.1007/s11370-023-00455-9 doi (DE-627)SPR050003844 (SPR)s11370-023-00455-9-e DE-627 ger DE-627 rakwb eng Liu, Yang verfasserin aut Object-aware data association for the semantically constrained visual SLAM 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. Visual SLAM (dpeaa)DE-He213 Robotic (dpeaa)DE-He213 Semantic SLAM (dpeaa)DE-He213 Computer vision (dpeaa)DE-He213 Guo, Chi aut Wang, Yingli aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 16(2023), 2 vom: 15. Feb., Seite 155-176 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:16 year:2023 number:2 day:15 month:02 pages:155-176 https://dx.doi.org/10.1007/s11370-023-00455-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 2 15 02 155-176
allfields_unstemmed	10.1007/s11370-023-00455-9 doi (DE-627)SPR050003844 (SPR)s11370-023-00455-9-e DE-627 ger DE-627 rakwb eng Liu, Yang verfasserin aut Object-aware data association for the semantically constrained visual SLAM 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. Visual SLAM (dpeaa)DE-He213 Robotic (dpeaa)DE-He213 Semantic SLAM (dpeaa)DE-He213 Computer vision (dpeaa)DE-He213 Guo, Chi aut Wang, Yingli aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 16(2023), 2 vom: 15. Feb., Seite 155-176 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:16 year:2023 number:2 day:15 month:02 pages:155-176 https://dx.doi.org/10.1007/s11370-023-00455-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 2 15 02 155-176
allfieldsGer	10.1007/s11370-023-00455-9 doi (DE-627)SPR050003844 (SPR)s11370-023-00455-9-e DE-627 ger DE-627 rakwb eng Liu, Yang verfasserin aut Object-aware data association for the semantically constrained visual SLAM 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. Visual SLAM (dpeaa)DE-He213 Robotic (dpeaa)DE-He213 Semantic SLAM (dpeaa)DE-He213 Computer vision (dpeaa)DE-He213 Guo, Chi aut Wang, Yingli aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 16(2023), 2 vom: 15. Feb., Seite 155-176 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:16 year:2023 number:2 day:15 month:02 pages:155-176 https://dx.doi.org/10.1007/s11370-023-00455-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 2 15 02 155-176
allfieldsSound	10.1007/s11370-023-00455-9 doi (DE-627)SPR050003844 (SPR)s11370-023-00455-9-e DE-627 ger DE-627 rakwb eng Liu, Yang verfasserin aut Object-aware data association for the semantically constrained visual SLAM 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. Visual SLAM (dpeaa)DE-He213 Robotic (dpeaa)DE-He213 Semantic SLAM (dpeaa)DE-He213 Computer vision (dpeaa)DE-He213 Guo, Chi aut Wang, Yingli aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 16(2023), 2 vom: 15. Feb., Seite 155-176 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:16 year:2023 number:2 day:15 month:02 pages:155-176 https://dx.doi.org/10.1007/s11370-023-00455-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 2 15 02 155-176
language	English
source	Enthalten in Intelligent service robotics 16(2023), 2 vom: 15. Feb., Seite 155-176 volume:16 year:2023 number:2 day:15 month:02 pages:155-176
sourceStr	Enthalten in Intelligent service robotics 16(2023), 2 vom: 15. Feb., Seite 155-176 volume:16 year:2023 number:2 day:15 month:02 pages:155-176
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Visual SLAM Robotic Semantic SLAM Computer vision
isfreeaccess_bool	false
container_title	Intelligent service robotics
authorswithroles_txt_mv	Liu, Yang @@aut@@ Guo, Chi @@aut@@ Wang, Yingli @@aut@@
publishDateDaySort_date	2023-02-15T00:00:00Z
hierarchy_top_id	537881409
id	SPR050003844
language_de	englisch
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author	Liu, Yang
spellingShingle	Liu, Yang misc Visual SLAM misc Robotic misc Semantic SLAM misc Computer vision Object-aware data association for the semantically constrained visual SLAM
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topic_title	Object-aware data association for the semantically constrained visual SLAM Visual SLAM (dpeaa)DE-He213 Robotic (dpeaa)DE-He213 Semantic SLAM (dpeaa)DE-He213 Computer vision (dpeaa)DE-He213
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title	Object-aware data association for the semantically constrained visual SLAM
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title_full	Object-aware data association for the semantically constrained visual SLAM
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author_browse	Liu, Yang Guo, Chi Wang, Yingli
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title_sort	object-aware data association for the semantically constrained visual slam
title_auth	Object-aware data association for the semantically constrained visual SLAM
abstract	Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. An open-source version of the code is also available. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Object-aware data association for the semantically constrained visual SLAM
url	https://dx.doi.org/10.1007/s11370-023-00455-9
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up_date	2024-07-04T03:05:22.881Z
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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">SPR050003844</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230412064659.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11370-023-00455-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR050003844</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11370-023-00455-9-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">Liu, Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Object-aware data association for the semantically constrained visual SLAM</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), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Traditional vSLAM methods extract feature points from images to track and the data association of points is based on low-level geometric clues. When points are observed from variant viewpoints, these clues are not robust for matching. In contrast, semantic information remains consistent for variance of viewpoints and observed scales. Therefore, semantic vSLAM methods gain more attention in recent years. In particular, object-level semantic information can be utilized to model the environment as object landmarks and has been fused into many vSLAM methods which are called object-level vSLAM methods. How to associate objects over consecutive images and how to utilize object information in the pose estimation are two key problems for object-level vSLAM methods. In this work, we propose an object-level vSLAM method which is aimed to solve the object-level data association and estimate camera poses using object semantic constraints. We present an object-level data association scheme considering object appearance and geometry of point landmarks, processing both objects and points matching for mutual improvements. We propose a semantic re-projection error function based on object-level semantic information and integrate it into the pose optimization, establishing longer term constraints. We performed experiments on public datasets including both indoor and outdoor scenes. The evaluation results demonstrate that our method can achieve high accuracy in the object-level data association and outperforms the baseline method in the pose estimation. 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