A Real-Time Map Restoration Algorithm Based on ORB-SLAM3

In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restor...
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Autor*in:	Weiwei Hu [verfasserIn] Qinglei Lin [verfasserIn] Lihuan Shao [verfasserIn] Jiaxu Lin [verfasserIn] Keke Zhang [verfasserIn] Huibin Qin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	visual-inertial SLAM ORB-SLAM3 initialization tracking bag-of-words MLPNP

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 12(2022), 15, p 7780
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:15, p 7780

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app12157780

Katalog-ID:	DOAJ016727223

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allfields	10.3390/app12157780 doi (DE-627)DOAJ016727223 (DE-599)DOAJ267bb509ef0742b1be7cd55dc7c49d48 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Weiwei Hu verfasserin aut A Real-Time Map Restoration Algorithm Based on ORB-SLAM3 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%. visual-inertial SLAM ORB-SLAM3 initialization tracking bag-of-words MLPNP Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Qinglei Lin verfasserin aut Lihuan Shao verfasserin aut Jiaxu Lin verfasserin aut Keke Zhang verfasserin aut Huibin Qin verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 15, p 7780 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:15, p 7780 https://doi.org/10.3390/app12157780 kostenfrei https://doaj.org/article/267bb509ef0742b1be7cd55dc7c49d48 kostenfrei https://www.mdpi.com/2076-3417/12/15/7780 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 12 2022 15, p 7780
spelling	10.3390/app12157780 doi (DE-627)DOAJ016727223 (DE-599)DOAJ267bb509ef0742b1be7cd55dc7c49d48 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Weiwei Hu verfasserin aut A Real-Time Map Restoration Algorithm Based on ORB-SLAM3 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%. visual-inertial SLAM ORB-SLAM3 initialization tracking bag-of-words MLPNP Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Qinglei Lin verfasserin aut Lihuan Shao verfasserin aut Jiaxu Lin verfasserin aut Keke Zhang verfasserin aut Huibin Qin verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 15, p 7780 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:15, p 7780 https://doi.org/10.3390/app12157780 kostenfrei https://doaj.org/article/267bb509ef0742b1be7cd55dc7c49d48 kostenfrei https://www.mdpi.com/2076-3417/12/15/7780 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 12 2022 15, p 7780
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allfieldsSound	10.3390/app12157780 doi (DE-627)DOAJ016727223 (DE-599)DOAJ267bb509ef0742b1be7cd55dc7c49d48 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Weiwei Hu verfasserin aut A Real-Time Map Restoration Algorithm Based on ORB-SLAM3 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%. visual-inertial SLAM ORB-SLAM3 initialization tracking bag-of-words MLPNP Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Qinglei Lin verfasserin aut Lihuan Shao verfasserin aut Jiaxu Lin verfasserin aut Keke Zhang verfasserin aut Huibin Qin verfasserin aut In Applied Sciences MDPI AG, 2012 12(2022), 15, p 7780 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:12 year:2022 number:15, p 7780 https://doi.org/10.3390/app12157780 kostenfrei https://doaj.org/article/267bb509ef0742b1be7cd55dc7c49d48 kostenfrei https://www.mdpi.com/2076-3417/12/15/7780 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 12 2022 15, p 7780
language	English
source	In Applied Sciences 12(2022), 15, p 7780 volume:12 year:2022 number:15, p 7780
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topic_facet	visual-inertial SLAM ORB-SLAM3 initialization tracking bag-of-words MLPNP Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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authorswithroles_txt_mv	Weiwei Hu @@aut@@ Qinglei Lin @@aut@@ Lihuan Shao @@aut@@ Jiaxu Lin @@aut@@ Keke Zhang @@aut@@ Huibin Qin @@aut@@
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callnumber-first	T - Technology
author	Weiwei Hu
spellingShingle	Weiwei Hu misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc visual-inertial SLAM misc ORB-SLAM3 misc initialization misc tracking misc bag-of-words misc MLPNP misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 A Real-Time Map Restoration Algorithm Based on ORB-SLAM3 visual-inertial SLAM ORB-SLAM3 initialization tracking bag-of-words MLPNP
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc visual-inertial SLAM misc ORB-SLAM3 misc initialization misc tracking misc bag-of-words misc MLPNP misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc visual-inertial SLAM misc ORB-SLAM3 misc initialization misc tracking misc bag-of-words misc MLPNP misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
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title_sort	real-time map restoration algorithm based on orb-slam3
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title_auth	A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
abstract	In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%.
abstractGer	In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%.
abstract_unstemmed	In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%.
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title_short	A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
url	https://doi.org/10.3390/app12157780 https://doaj.org/article/267bb509ef0742b1be7cd55dc7c49d48 https://www.mdpi.com/2076-3417/12/15/7780 https://doaj.org/toc/2076-3417
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A Real-Time Map Restoration Algorithm Based on ORB-SLAM3

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