Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment
Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large a...
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| Veröffentlicht in: | Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-13 |
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| creator | Yin, Dan Zhou, Siwei Wang, Pengcheng Lin, Manling Luo, Kaiqing Zhang, Haolan |
| description | Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information. |
| doi_str_mv | 10.1155/2020/4724310 |
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However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/4724310</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Adaptive algorithms ; Algorithms ; Cameras ; Entropy (Information theory) ; Lasers ; Motion effects ; Neural networks ; Posture ; Real time ; Semantics ; Sensors ; Sharpening ; Simultaneous localization and mapping ; Texture ; Trajectories</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-13</ispartof><rights>Copyright © 2020 Kaiqing Luo et al.</rights><rights>Copyright © 2020 Kaiqing Luo et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-e766320e91f44d2c5b12880e2680c6d27773a9d2691c7f63777c02cb41560f613</citedby><cites>FETCH-LOGICAL-c360t-e766320e91f44d2c5b12880e2680c6d27773a9d2691c7f63777c02cb41560f613</cites><orcidid>0000-0002-6278-0917 ; 0000-0002-0367-2123 ; 0000-0002-5033-1866 ; 0000-0001-5398-5956 ; 0000-0001-5301-7365 ; 0000-0003-3680-5997</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,4012,27906,27907,27908</link.rule.ids></links><search><contributor>Lee, Sanghyuk</contributor><contributor>Sanghyuk Lee</contributor><creatorcontrib>Yin, Dan</creatorcontrib><creatorcontrib>Zhou, Siwei</creatorcontrib><creatorcontrib>Wang, Pengcheng</creatorcontrib><creatorcontrib>Lin, Manling</creatorcontrib><creatorcontrib>Luo, Kaiqing</creatorcontrib><creatorcontrib>Zhang, Haolan</creatorcontrib><title>Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment</title><title>Mathematical problems in engineering</title><description>Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. 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To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information.</description><subject>Accuracy</subject><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Cameras</subject><subject>Entropy (Information theory)</subject><subject>Lasers</subject><subject>Motion effects</subject><subject>Neural networks</subject><subject>Posture</subject><subject>Real time</subject><subject>Semantics</subject><subject>Sensors</subject><subject>Sharpening</subject><subject>Simultaneous localization and mapping</subject><subject>Texture</subject><subject>Trajectories</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0EFLwzAUB_AgCs7pzbMEPGpdXpIm7bEbUwuTgVPQU8nadOtY05p0yr69GR149JQX3o_3eH-EroE8AIThiBJKRlxSzoCcoAGEggUhcHnqa0J5AJR9nKML5zaEUAghGqDPtG5t860LPH8dB4tZ8kJxsl01turWNR4r5zuNwakpG1urrvL11HS2afdYmQKntVppvFgr22pTmRVOis3OdbU23SU6K9XW6avjO0Tvj9O3yXMwmz-lk2QW5EyQLtBSCEaJjqHkvKB5uAQaRURTEZFcFFRKyVRcUBFDLkvB_D8nNF9yfx0pBbAhuu3n-ju-dtp12abZWeNXZpTziAoSR8Kr-17ltnHO6jJrbVUru8-AZIfwskN42TE8z-96vq5MoX6q__RNr7U3ulR_GuKQSWC_qS11uw</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Yin, Dan</creator><creator>Zhou, Siwei</creator><creator>Wang, Pengcheng</creator><creator>Lin, Manling</creator><creator>Luo, Kaiqing</creator><creator>Zhang, Haolan</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-6278-0917</orcidid><orcidid>https://orcid.org/0000-0002-0367-2123</orcidid><orcidid>https://orcid.org/0000-0002-5033-1866</orcidid><orcidid>https://orcid.org/0000-0001-5398-5956</orcidid><orcidid>https://orcid.org/0000-0001-5301-7365</orcidid><orcidid>https://orcid.org/0000-0003-3680-5997</orcidid></search><sort><creationdate>2020</creationdate><title>Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment</title><author>Yin, Dan ; 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| subjects | Accuracy Adaptive algorithms Algorithms Cameras Entropy (Information theory) Lasers Motion effects Neural networks Posture Real time Semantics Sensors Sharpening Simultaneous localization and mapping Texture Trajectories |
| title | Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment |
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