Research on key frame image processing of semantic SLAM based on deep learning

Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic i...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	DENG Chen [verfasserIn] LI Hongwei [verfasserIn] ZHANG Bin [verfasserIn] XU Zhibin [verfasserIn] XIAO Zhiyuan [verfasserIn]

Format:	E-Artikel
Sprache:	Chinesisch

Erschienen:	2021

Schlagwörter:	simultaneous localization and mapping orb-slam3 three-dimensional semantic map deep learning mask r-cnn

Übergeordnetes Werk:	In: Acta Geodaetica et Cartographica Sinica - Surveying and Mapping Press, 2014, 50(2021), 11, Seite 1605-1616
Übergeordnetes Werk:	volume:50 ; year:2021 ; number:11 ; pages:1605-1616

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.11947/j.AGCS.2021.20210251

Katalog-ID:	DOAJ015900517

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520			\|a Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots.
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spelling	10.11947/j.AGCS.2021.20210251 doi (DE-627)DOAJ015900517 (DE-599)DOAJ8d28021f780a40f796e87489058d74e8 DE-627 ger DE-627 rakwb chi GA1-1776 DENG Chen verfasserin aut Research on key frame image processing of semantic SLAM based on deep learning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots. simultaneous localization and mapping orb-slam3 three-dimensional semantic map deep learning mask r-cnn Mathematical geography. Cartography LI Hongwei verfasserin aut ZHANG Bin verfasserin aut XU Zhibin verfasserin aut XIAO Zhiyuan verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 50(2021), 11, Seite 1605-1616 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:50 year:2021 number:11 pages:1605-1616 https://doi.org/10.11947/j.AGCS.2021.20210251 kostenfrei https://doaj.org/article/8d28021f780a40f796e87489058d74e8 kostenfrei http://xb.sinomaps.com/article/2021/1001-1595/2021-11-1605.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4392 GBV_ILN_4700 AR 50 2021 11 1605-1616
allfields_unstemmed	10.11947/j.AGCS.2021.20210251 doi (DE-627)DOAJ015900517 (DE-599)DOAJ8d28021f780a40f796e87489058d74e8 DE-627 ger DE-627 rakwb chi GA1-1776 DENG Chen verfasserin aut Research on key frame image processing of semantic SLAM based on deep learning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots. simultaneous localization and mapping orb-slam3 three-dimensional semantic map deep learning mask r-cnn Mathematical geography. Cartography LI Hongwei verfasserin aut ZHANG Bin verfasserin aut XU Zhibin verfasserin aut XIAO Zhiyuan verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 50(2021), 11, Seite 1605-1616 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:50 year:2021 number:11 pages:1605-1616 https://doi.org/10.11947/j.AGCS.2021.20210251 kostenfrei https://doaj.org/article/8d28021f780a40f796e87489058d74e8 kostenfrei http://xb.sinomaps.com/article/2021/1001-1595/2021-11-1605.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4392 GBV_ILN_4700 AR 50 2021 11 1605-1616
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source	In Acta Geodaetica et Cartographica Sinica 50(2021), 11, Seite 1605-1616 volume:50 year:2021 number:11 pages:1605-1616
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callnumber-first	G - Geography, Anthropology, Recreation
author	DENG Chen
spellingShingle	DENG Chen misc GA1-1776 misc simultaneous localization and mapping misc orb-slam3 misc three-dimensional semantic map misc deep learning misc mask r-cnn misc Mathematical geography. Cartography Research on key frame image processing of semantic SLAM based on deep learning
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topic_title	GA1-1776 Research on key frame image processing of semantic SLAM based on deep learning simultaneous localization and mapping orb-slam3 three-dimensional semantic map deep learning mask r-cnn
topic	misc GA1-1776 misc simultaneous localization and mapping misc orb-slam3 misc three-dimensional semantic map misc deep learning misc mask r-cnn misc Mathematical geography. Cartography
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title	Research on key frame image processing of semantic SLAM based on deep learning
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title_full	Research on key frame image processing of semantic SLAM based on deep learning
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title_sort	research on key frame image processing of semantic slam based on deep learning
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title_auth	Research on key frame image processing of semantic SLAM based on deep learning
abstract	Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots.
abstractGer	Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots.
abstract_unstemmed	Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots.
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title_short	Research on key frame image processing of semantic SLAM based on deep learning
url	https://doi.org/10.11947/j.AGCS.2021.20210251 https://doaj.org/article/8d28021f780a40f796e87489058d74e8 http://xb.sinomaps.com/article/2021/1001-1595/2021-11-1605.htm https://doaj.org/toc/1001-1595
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Research on key frame image processing of semantic SLAM based on deep learning

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