A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method

In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the featur...
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Autor*in:	Haiyang Qiu [verfasserIn] Xu Zhang [verfasserIn] Hui Wang [verfasserIn] Dan Xiang [verfasserIn] Mingming Xiao [verfasserIn] Zhiyu Zhu [verfasserIn] Lei Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	visual odometry optical flow tracking feature point method ORB_SLAM3

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 8655, p 8655
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:8655, p 8655

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23208655

Katalog-ID:	DOAJ093078366

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allfields	10.3390/s23208655 doi (DE-627)DOAJ093078366 (DE-599)DOAJ0a2d794521184ee981e9ece99e866e49 DE-627 ger DE-627 rakwb eng TP1-1185 Haiyang Qiu verfasserin aut A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach. visual odometry optical flow tracking feature point method ORB_SLAM3 Chemical technology Xu Zhang verfasserin aut Hui Wang verfasserin aut Dan Xiang verfasserin aut Mingming Xiao verfasserin aut Zhiyu Zhu verfasserin aut Lei Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8655, p 8655 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8655, p 8655 https://doi.org/10.3390/s23208655 kostenfrei https://doaj.org/article/0a2d794521184ee981e9ece99e866e49 kostenfrei https://www.mdpi.com/1424-8220/23/20/8655 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8655, p 8655
spelling	10.3390/s23208655 doi (DE-627)DOAJ093078366 (DE-599)DOAJ0a2d794521184ee981e9ece99e866e49 DE-627 ger DE-627 rakwb eng TP1-1185 Haiyang Qiu verfasserin aut A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach. visual odometry optical flow tracking feature point method ORB_SLAM3 Chemical technology Xu Zhang verfasserin aut Hui Wang verfasserin aut Dan Xiang verfasserin aut Mingming Xiao verfasserin aut Zhiyu Zhu verfasserin aut Lei Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8655, p 8655 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8655, p 8655 https://doi.org/10.3390/s23208655 kostenfrei https://doaj.org/article/0a2d794521184ee981e9ece99e866e49 kostenfrei https://www.mdpi.com/1424-8220/23/20/8655 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8655, p 8655
allfields_unstemmed	10.3390/s23208655 doi (DE-627)DOAJ093078366 (DE-599)DOAJ0a2d794521184ee981e9ece99e866e49 DE-627 ger DE-627 rakwb eng TP1-1185 Haiyang Qiu verfasserin aut A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach. visual odometry optical flow tracking feature point method ORB_SLAM3 Chemical technology Xu Zhang verfasserin aut Hui Wang verfasserin aut Dan Xiang verfasserin aut Mingming Xiao verfasserin aut Zhiyu Zhu verfasserin aut Lei Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8655, p 8655 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8655, p 8655 https://doi.org/10.3390/s23208655 kostenfrei https://doaj.org/article/0a2d794521184ee981e9ece99e866e49 kostenfrei https://www.mdpi.com/1424-8220/23/20/8655 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8655, p 8655
allfieldsGer	10.3390/s23208655 doi (DE-627)DOAJ093078366 (DE-599)DOAJ0a2d794521184ee981e9ece99e866e49 DE-627 ger DE-627 rakwb eng TP1-1185 Haiyang Qiu verfasserin aut A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach. visual odometry optical flow tracking feature point method ORB_SLAM3 Chemical technology Xu Zhang verfasserin aut Hui Wang verfasserin aut Dan Xiang verfasserin aut Mingming Xiao verfasserin aut Zhiyu Zhu verfasserin aut Lei Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8655, p 8655 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8655, p 8655 https://doi.org/10.3390/s23208655 kostenfrei https://doaj.org/article/0a2d794521184ee981e9ece99e866e49 kostenfrei https://www.mdpi.com/1424-8220/23/20/8655 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8655, p 8655
allfieldsSound	10.3390/s23208655 doi (DE-627)DOAJ093078366 (DE-599)DOAJ0a2d794521184ee981e9ece99e866e49 DE-627 ger DE-627 rakwb eng TP1-1185 Haiyang Qiu verfasserin aut A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach. visual odometry optical flow tracking feature point method ORB_SLAM3 Chemical technology Xu Zhang verfasserin aut Hui Wang verfasserin aut Dan Xiang verfasserin aut Mingming Xiao verfasserin aut Zhiyu Zhu verfasserin aut Lei Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8655, p 8655 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8655, p 8655 https://doi.org/10.3390/s23208655 kostenfrei https://doaj.org/article/0a2d794521184ee981e9ece99e866e49 kostenfrei https://www.mdpi.com/1424-8220/23/20/8655 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8655, p 8655
language	English
source	In Sensors 23(2023), 8655, p 8655 volume:23 year:2023 number:8655, p 8655
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authorswithroles_txt_mv	Haiyang Qiu @@aut@@ Xu Zhang @@aut@@ Hui Wang @@aut@@ Dan Xiang @@aut@@ Mingming Xiao @@aut@@ Zhiyu Zhu @@aut@@ Lei Wang @@aut@@
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callnumber-first	T - Technology
author	Haiyang Qiu
spellingShingle	Haiyang Qiu misc TP1-1185 misc visual odometry misc optical flow tracking misc feature point method misc ORB_SLAM3 misc Chemical technology A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method
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topic_title	TP1-1185 A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method visual odometry optical flow tracking feature point method ORB_SLAM3
topic	misc TP1-1185 misc visual odometry misc optical flow tracking misc feature point method misc ORB_SLAM3 misc Chemical technology
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title	A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method
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title_sort	robust and integrated visual odometry framework exploiting the optical flow and feature point method
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title_auth	A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method
abstract	In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach.
abstractGer	In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach.
abstract_unstemmed	In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach.
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title_short	A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method
url	https://doi.org/10.3390/s23208655 https://doaj.org/article/0a2d794521184ee981e9ece99e866e49 https://www.mdpi.com/1424-8220/23/20/8655 https://doaj.org/toc/1424-8220
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author2	Xu Zhang Hui Wang Dan Xiang Mingming Xiao Zhiyu Zhu Lei Wang
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doi_str	10.3390/s23208655
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up_date	2024-07-03T15:08:18.892Z
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A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method

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