Enhancing Robustness and Accuracy in Edge-Assisted Visual SLAM Implementation
With the increasing demand for spatial positioning on modern mobile devices, Simultaneous Localization and Mapping (SLAM), particularly camera-based Visual SLAM, has become essential for real-time perception and positioning by processing continuous image data. However,these algorithms often entail h...
Saved in:
| Published in | International Journal of Networking and Computing Vol. 15; no. 2; pp. 85 - 101 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
IJNC Editorial Committee
2025
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2185-2839 2185-2847 |
| DOI | 10.15803/ijnc.15.2_85 |
Cover
| Abstract | With the increasing demand for spatial positioning on modern mobile devices, Simultaneous Localization and Mapping (SLAM), particularly camera-based Visual SLAM, has become essential for real-time perception and positioning by processing continuous image data. However,these algorithms often entail high memory and computational requirements, making it challenging to deploy them on mobile devices and run for extended periods. To address this issue, the edge-assisted SLAM architecture, which offloads computationally intensive tasks toedge servers, has been proposed. Despite its potential, existing solutions in this domain suffer from significant limitations in data synchronization and recovery capability, compromising both the robustness and accuracy of the system. In response to the identified limitations, we analyze the impact of the current data synchronization and relocalization recovery processes on system performance, and introduce a novel multithreaded tracking approach integrated with an efficient relocalization mechanism. We validated our approach in standard datasets, including the robustness of the system, tracking recovery capability, and localization accuracy. Experimentalresults demonstrate that our solution reduces tracking interruptions by up to 94.2%, significantly improves coverage, a vital robustness metric of the SLAM system, by up to 30.1%, and shortens relocalization recovery time by up to 35.2%. Furthermore, our approach improvesthe localization accuracy by 43.7% in translation scenarios and 36.8% in rotation scenarios. |
|---|---|
| AbstractList | With the increasing demand for spatial positioning on modern mobile devices, Simultaneous Localization and Mapping (SLAM), particularly camera-based Visual SLAM, has become essential for real-time perception and positioning by processing continuous image data. However,these algorithms often entail high memory and computational requirements, making it challenging to deploy them on mobile devices and run for extended periods. To address this issue, the edge-assisted SLAM architecture, which offloads computationally intensive tasks toedge servers, has been proposed. Despite its potential, existing solutions in this domain suffer from significant limitations in data synchronization and recovery capability, compromising both the robustness and accuracy of the system. In response to the identified limitations, we analyze the impact of the current data synchronization and relocalization recovery processes on system performance, and introduce a novel multithreaded tracking approach integrated with an efficient relocalization mechanism. We validated our approach in standard datasets, including the robustness of the system, tracking recovery capability, and localization accuracy. Experimentalresults demonstrate that our solution reduces tracking interruptions by up to 94.2%, significantly improves coverage, a vital robustness metric of the SLAM system, by up to 30.1%, and shortens relocalization recovery time by up to 35.2%. Furthermore, our approach improvesthe localization accuracy by 43.7% in translation scenarios and 36.8% in rotation scenarios. |
| Author | Chenzhang Xia Koji Inoue Yuan Wang |
| Author_xml | – sequence: 1 givenname: Chenzhang surname: Xia fullname: Xia, Chenzhang – sequence: 2 givenname: Yuan surname: Wang fullname: Wang, Yuan – sequence: 3 givenname: Koji surname: Inoue fullname: Inoue, Koji |
| BookMark | eNpNkE1rAjEQhkOxUGs99p4_sDbJmmz20MMitgpKoV_XMJvMamTNymY9-O-71iKdw8zL8DAMzz0ZhCYgIY-cTbjULH3yu2D7OBFGyxsyFFzLROhpNrjmNL8j4xh3rK8sy5lIh2Q9D1sI1ocNfW_KY-wCxkghOFpYe2zBnqgPdO42mBQx-tiho98-HqGmH6tiTZf7Q417DB10vgkP5LaCOuL4b47I18v8c7ZIVm-vy1mxSqzgMiRQKadLDqoqS4ZVyR2gzjMrc8al4Ay16D8H7RhINc05A6GmSqFTDmyGkI5Icrlr2ybGFitzaP0e2pPhzPzqMGcdfTRnHT3_fOF3sYMNXmloO29r_Af3Tcvr3m6hNRjSH8ycbHg |
| Cites_doi | 10.1109/ICCV.2011.6126517 10.1109/ICASERT.2019.8934466 10.1109/ICRA.2014.6906953 10.1109/GLOBECOM48099.2022.10001128 10.3390/robotics11010024 10.1109/TPAMI.2011.41 10.1145/3561972 10.1109/TRO.2017.2705103 10.1109/IROS.2012.6385773 10.1109/VR.2016.7504740 10.1109/MMSP48831.2020.9287125 10.1109/ACCESS.2021.3049864 10.1109/RCAR.2017.8311876 10.1186/s13638-022-02181-9 10.1109/TRO.2015.2463671 10.1109/ICIP.2013.6738525 10.1109/MASS56207.2022.00071 10.1109/SEC50012.2020.00018 10.1109/ICOIN56518.2023.10048921 10.1007/s40747-020-00161-4 10.1109/ICCV.2011.6126544 10.1016/j.array.2022.100222 10.1007/978-3-319-10605-2_54 10.1145/3004010.3004032 10.1007/3-540-44480-7_21 10.1007/978-981-99-1814-0_2 10.1109/ACCESS.2022.3218774 10.1109/ICC45041.2023.10278556 |
| ContentType | Journal Article |
| Copyright | 2025 International Journal of Networking and Computing |
| Copyright_xml | – notice: 2025 International Journal of Networking and Computing |
| DBID | AAYXX CITATION |
| DOI | 10.15803/ijnc.15.2_85 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2185-2847 |
| EndPage | 101 |
| ExternalDocumentID | 10_15803_ijnc_15_2_85 article_ijnc_15_2_15_85_article_char_en |
| GroupedDBID | 7.U ALMA_UNASSIGNED_HOLDINGS JSF JSH KQ8 KWQ OK1 RJT RZJ AAYXX CITATION ISHAI |
| ID | FETCH-LOGICAL-c215n-af6d8b1a6fbb0efb1dae897c59015210e82284a8d0a564910a26466ed6dac7ea3 |
| ISSN | 2185-2839 |
| IngestDate | Wed Oct 01 05:46:01 EDT 2025 Wed Sep 03 06:30:40 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c215n-af6d8b1a6fbb0efb1dae897c59015210e82284a8d0a564910a26466ed6dac7ea3 |
| OpenAccessLink | https://www.jstage.jst.go.jp/article/ijnc/15/2/15_85/_article/-char/en |
| PageCount | 17 |
| ParticipantIDs | crossref_primary_10_15803_ijnc_15_2_85 jstage_primary_article_ijnc_15_2_15_85_article_char_en |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025 2025-00-00 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | International Journal of Networking and Computing |
| PublicationTitleAlternate | IJNC |
| PublicationYear | 2025 |
| Publisher | IJNC Editorial Committee |
| Publisher_xml | – name: IJNC Editorial Committee |
| References | [28] Gang Peng, Tin Lun Lam, Chunxu Hu, Yu Yao, Jintao Liu, and Fan Yang. Connecting the robot to ros. In Introduction to Intelligent Robot System Design: Application Development with ROS, pages 41–137, 2023. [14] Swarnava Dey and Arijit Mukherjee. Robotic slam: a review from fog computing and mobile edge computing perspective. In Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services, pages 153–158, 2016. [11] Ali J Ben Ali, Marziye Kouroshli, Sofiya Semenova, Zakieh Sadat Hashemifar, Steven Y Ko, and Karthik Dantu. Edge-slam: Edge-assisted visual simultaneous localization and mapping. ACM Transactions on Embedded Computing Systems, 22(1):1–31, 2022. [20] Raúl Mur-Artal and Juan D Tardós. Fast relocalisation and loop closing in keyframe-based slam. In 2014 IEEE International Conference on Robotics and Automation (ICRA), pages 846–853, 2014. [18] Brian Williams, Georg Klein, and Ian Reid. Automatic relocalization and loop closing for real-time monocular slam. IEEE transactions on pattern analysis and machine intelligence, 33(9):1699–1712, 2011. [8] Jingao Xu, Zheng Yang, Yunhao Liu, and Hao Cao. Edge assisted mobile visual slam, 2024. [4] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147–1163, 2015. [25] Johannes Hofer, Peter Sossalla, Justus Rischke, Christian L Vielhaus, Martin Reisslein, and Frank HP Fitzek. Circular frame buffer to enhance map synchronization in edge assisted slam. In ICC 2023-IEEE International Conference on Communications, pages 210–215, 2023. [3] Jiansheng Peng, Yaru Hou, Hengming Xu, and Taotao Li. Dynamic visual slam and mec technologies for b5g: a comprehensive review. EURASIP Journal on Wireless Communications and Networking, 2022(1):98, 2022. [24] Atsunori Moteki, Nobuyasu Yamaguchi, Ayu Karasudani, and Toshiyuki Yoshitake. Fast and accurate relocalization for keyframe-based slam using geometric model selection. In 2016 IEEE Virtual Reality (VR), pages 235–236, 2016. [26] Pengfei Zhang, Huaimin Wang, and Bo Ding. Using collaborative sharing on cloud for fast relocalization in keyframe-based slam. In 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR), pages 291–296, 2017. [19] Hauke Strasdat, Andrew J Davison, JM Martìnez Montiel, and Kurt Konolige. Double window optimisation for constant time visual slam. In 2011 international conference on computer vision, pages 2352–2359, 2011. [9] Peter Sossalla, Johannes Hofer, Justus Rischke, Christian Vielhaus, Giang T Nguyen, Martin Reisslein, and Frank HP Fitzek. Dynnetslam: Dynamic visual slam network offloading. IEEE Access, 10:116014–116030, 2022. [16] Kwame-Lante Wright, Ashiwan Sivakumar, Peter Steenkiste, Bo Yu, and Fan Bai. Cloudslam: Edge offloading of stateful vehicular applications. In 2020 IEEE/ACM Symposium on Edge Computing (SEC), pages 139–151, 2020. [21] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International Conference on Computer Vision, pages 2564–2571, 2011. [29] Zongqian Zhan, Wenjie Jian, Yihui Li, and Yang Yue. A slam map restoration algorithm based on submaps and an undirected connected graph. IEEE Access, 9:12657–12674, 2021. [10] Timothy Chase, Ali J Ben Ali, Steven Y Ko, and Karthik Dantu. Pre-slam: Persistence reasoning in edge-assisted visual slam. In 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pages 458–466, 2022. [17] Peter Sossalla, Johannes Hofer, Justus Rischke, Johannes Busch, Giang T Nguyen, Martin Reisslein, and Frank HP Fitzek. Optimizing edge slam: Judicious parameter settings and parallelized map updates. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 1954–1959, 2020. [2] Andréa Macario Barros, Maugan Michel, Yoann Moline, Gwenolé Corre, and Frédérick Carrel. A comprehensive survey of visual slam algorithms. Robotics, 11(1):24, 2022. [15] Victor Kathan Sarker, J Pe na Queralta, Tuan Nguyen Gia, Hannu Tenhunen, and Tomi Westerlund. Offloading slam for indoor mobile robots with edge-fog-cloud computing. In 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), pages 1–6, 2019. [1] Charalambos Theodorou, Vladan Velisavljevic, Vladimir Dyo, and Fredi Nonyelu. Visual slam algorithms and their application for ar, mapping, localization and wayfinding. Array, 15:100–222, 2022. [5] Jakob Engel, Thomas Schöps, and Daniel Cremers. Lsd-slam: Large-scale direct monocular slam. In European conference on computer vision, pages 834–849, 2014. [12] Shuai Liu, Dongye Liu, Gautam Srivastava, Dawid Poap, and Marcin Wożniak. Overview and methods of correlation filter algorithms in object tracking. Complex & Intelligent Systems, 7:1895–1917, 2021. [23] Jeremy Straub, Sebastian Hilsenbeck, Georg Schroth, Robert Huitl, Andreas Möller, and Eckehard Steinbach. Fast relocalization for visual odometry using binary features. In 2013 IEEE International Conference on Image Processing, pages 2548–2552, 2013. [6] Sebastian Eger, Rastin Pries, and Eckehard Steinbach. valuation of different task distributions for edge cloud-based collaborative visual slam. In 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), pages 1–6, 2020. [22] Bill Triggs, Philip F. McLauchlan, Richard I. Hartley, and Andrew W. Fitzgibbon. Bundle adjustment –-a modern synthesis. In Bill Triggs, Andrew Zisserman, and Richard Szeliski, editors, Vision Algorithms: Theory and Practice, pages 298–372, Berlin, Heidelberg, 2000. Springer Berlin Heidelberg. [7] Peter Sossalla, Johannes Hofer, Christian Vielhaus, Justus Rischke, and Frank HP Fitzek. Offloading visual slam processing to the edge: An energy perspective. In 2023 International Conference on Information Networking (ICOIN), pages 39–44, 2023. [13] Raul Mur-Artal and Juan D Tardós. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE transactions on robotics, 33(5):1255–1262, 2017. [27] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pages 573–580, 2012. 22 23 24 25 26 27 28 29 10 11 12 13 14 15 16 17 18 19 1 2 3 4 5 6 7 8 9 20 21 |
| References_xml | – reference: [20] Raúl Mur-Artal and Juan D Tardós. Fast relocalisation and loop closing in keyframe-based slam. In 2014 IEEE International Conference on Robotics and Automation (ICRA), pages 846–853, 2014. – reference: [14] Swarnava Dey and Arijit Mukherjee. Robotic slam: a review from fog computing and mobile edge computing perspective. In Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services, pages 153–158, 2016. – reference: [18] Brian Williams, Georg Klein, and Ian Reid. Automatic relocalization and loop closing for real-time monocular slam. IEEE transactions on pattern analysis and machine intelligence, 33(9):1699–1712, 2011. – reference: [2] Andréa Macario Barros, Maugan Michel, Yoann Moline, Gwenolé Corre, and Frédérick Carrel. A comprehensive survey of visual slam algorithms. Robotics, 11(1):24, 2022. – reference: [25] Johannes Hofer, Peter Sossalla, Justus Rischke, Christian L Vielhaus, Martin Reisslein, and Frank HP Fitzek. Circular frame buffer to enhance map synchronization in edge assisted slam. In ICC 2023-IEEE International Conference on Communications, pages 210–215, 2023. – reference: [21] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International Conference on Computer Vision, pages 2564–2571, 2011. – reference: [28] Gang Peng, Tin Lun Lam, Chunxu Hu, Yu Yao, Jintao Liu, and Fan Yang. Connecting the robot to ros. In Introduction to Intelligent Robot System Design: Application Development with ROS, pages 41–137, 2023. – reference: [4] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147–1163, 2015. – reference: [27] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pages 573–580, 2012. – reference: [13] Raul Mur-Artal and Juan D Tardós. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE transactions on robotics, 33(5):1255–1262, 2017. – reference: [11] Ali J Ben Ali, Marziye Kouroshli, Sofiya Semenova, Zakieh Sadat Hashemifar, Steven Y Ko, and Karthik Dantu. Edge-slam: Edge-assisted visual simultaneous localization and mapping. ACM Transactions on Embedded Computing Systems, 22(1):1–31, 2022. – reference: [26] Pengfei Zhang, Huaimin Wang, and Bo Ding. Using collaborative sharing on cloud for fast relocalization in keyframe-based slam. In 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR), pages 291–296, 2017. – reference: [6] Sebastian Eger, Rastin Pries, and Eckehard Steinbach. valuation of different task distributions for edge cloud-based collaborative visual slam. In 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), pages 1–6, 2020. – reference: [19] Hauke Strasdat, Andrew J Davison, JM Martìnez Montiel, and Kurt Konolige. Double window optimisation for constant time visual slam. In 2011 international conference on computer vision, pages 2352–2359, 2011. – reference: [1] Charalambos Theodorou, Vladan Velisavljevic, Vladimir Dyo, and Fredi Nonyelu. Visual slam algorithms and their application for ar, mapping, localization and wayfinding. Array, 15:100–222, 2022. – reference: [12] Shuai Liu, Dongye Liu, Gautam Srivastava, Dawid Poap, and Marcin Wożniak. Overview and methods of correlation filter algorithms in object tracking. Complex & Intelligent Systems, 7:1895–1917, 2021. – reference: [24] Atsunori Moteki, Nobuyasu Yamaguchi, Ayu Karasudani, and Toshiyuki Yoshitake. Fast and accurate relocalization for keyframe-based slam using geometric model selection. In 2016 IEEE Virtual Reality (VR), pages 235–236, 2016. – reference: [5] Jakob Engel, Thomas Schöps, and Daniel Cremers. Lsd-slam: Large-scale direct monocular slam. In European conference on computer vision, pages 834–849, 2014. – reference: [7] Peter Sossalla, Johannes Hofer, Christian Vielhaus, Justus Rischke, and Frank HP Fitzek. Offloading visual slam processing to the edge: An energy perspective. In 2023 International Conference on Information Networking (ICOIN), pages 39–44, 2023. – reference: [22] Bill Triggs, Philip F. McLauchlan, Richard I. Hartley, and Andrew W. Fitzgibbon. Bundle adjustment –-a modern synthesis. In Bill Triggs, Andrew Zisserman, and Richard Szeliski, editors, Vision Algorithms: Theory and Practice, pages 298–372, Berlin, Heidelberg, 2000. Springer Berlin Heidelberg. – reference: [29] Zongqian Zhan, Wenjie Jian, Yihui Li, and Yang Yue. A slam map restoration algorithm based on submaps and an undirected connected graph. IEEE Access, 9:12657–12674, 2021. – reference: [9] Peter Sossalla, Johannes Hofer, Justus Rischke, Christian Vielhaus, Giang T Nguyen, Martin Reisslein, and Frank HP Fitzek. Dynnetslam: Dynamic visual slam network offloading. IEEE Access, 10:116014–116030, 2022. – reference: [10] Timothy Chase, Ali J Ben Ali, Steven Y Ko, and Karthik Dantu. Pre-slam: Persistence reasoning in edge-assisted visual slam. In 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pages 458–466, 2022. – reference: [15] Victor Kathan Sarker, J Pe na Queralta, Tuan Nguyen Gia, Hannu Tenhunen, and Tomi Westerlund. Offloading slam for indoor mobile robots with edge-fog-cloud computing. In 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), pages 1–6, 2019. – reference: [17] Peter Sossalla, Johannes Hofer, Justus Rischke, Johannes Busch, Giang T Nguyen, Martin Reisslein, and Frank HP Fitzek. Optimizing edge slam: Judicious parameter settings and parallelized map updates. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 1954–1959, 2020. – reference: [8] Jingao Xu, Zheng Yang, Yunhao Liu, and Hao Cao. Edge assisted mobile visual slam, 2024. – reference: [3] Jiansheng Peng, Yaru Hou, Hengming Xu, and Taotao Li. Dynamic visual slam and mec technologies for b5g: a comprehensive review. EURASIP Journal on Wireless Communications and Networking, 2022(1):98, 2022. – reference: [23] Jeremy Straub, Sebastian Hilsenbeck, Georg Schroth, Robert Huitl, Andreas Möller, and Eckehard Steinbach. Fast relocalization for visual odometry using binary features. In 2013 IEEE International Conference on Image Processing, pages 2548–2552, 2013. – reference: [16] Kwame-Lante Wright, Ashiwan Sivakumar, Peter Steenkiste, Bo Yu, and Fan Bai. Cloudslam: Edge offloading of stateful vehicular applications. In 2020 IEEE/ACM Symposium on Edge Computing (SEC), pages 139–151, 2020. – ident: 19 doi: 10.1109/ICCV.2011.6126517 – ident: 15 doi: 10.1109/ICASERT.2019.8934466 – ident: 20 doi: 10.1109/ICRA.2014.6906953 – ident: 17 doi: 10.1109/GLOBECOM48099.2022.10001128 – ident: 2 doi: 10.3390/robotics11010024 – ident: 18 doi: 10.1109/TPAMI.2011.41 – ident: 11 doi: 10.1145/3561972 – ident: 13 doi: 10.1109/TRO.2017.2705103 – ident: 27 doi: 10.1109/IROS.2012.6385773 – ident: 24 doi: 10.1109/VR.2016.7504740 – ident: 6 doi: 10.1109/MMSP48831.2020.9287125 – ident: 29 doi: 10.1109/ACCESS.2021.3049864 – ident: 26 doi: 10.1109/RCAR.2017.8311876 – ident: 3 doi: 10.1186/s13638-022-02181-9 – ident: 4 doi: 10.1109/TRO.2015.2463671 – ident: 23 doi: 10.1109/ICIP.2013.6738525 – ident: 10 doi: 10.1109/MASS56207.2022.00071 – ident: 16 doi: 10.1109/SEC50012.2020.00018 – ident: 7 doi: 10.1109/ICOIN56518.2023.10048921 – ident: 12 doi: 10.1007/s40747-020-00161-4 – ident: 21 doi: 10.1109/ICCV.2011.6126544 – ident: 1 doi: 10.1016/j.array.2022.100222 – ident: 5 doi: 10.1007/978-3-319-10605-2_54 – ident: 14 doi: 10.1145/3004010.3004032 – ident: 22 doi: 10.1007/3-540-44480-7_21 – ident: 28 doi: 10.1007/978-981-99-1814-0_2 – ident: 9 doi: 10.1109/ACCESS.2022.3218774 – ident: 8 – ident: 25 doi: 10.1109/ICC45041.2023.10278556 |
| SSID | ssj0000779023 |
| Score | 2.2810516 |
| Snippet | With the increasing demand for spatial positioning on modern mobile devices, Simultaneous Localization and Mapping (SLAM), particularly camera-based Visual... |
| SourceID | crossref jstage |
| SourceType | Index Database Publisher |
| StartPage | 85 |
| SubjectTerms | edge assisted slam edge computing multithreaded system slam relocalization thread refactoring visual slam |
| Title | Enhancing Robustness and Accuracy in Edge-Assisted Visual SLAM Implementation |
| URI | https://www.jstage.jst.go.jp/article/ijnc/15/2/15_85/_article/-char/en |
| Volume | 15 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | International Journal of Networking and Computing, 2025, Vol.15(2), pp.85-101 |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 2185-2847 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000779023 issn: 2185-2839 databaseCode: KQ8 dateStart: 20110101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07b9swECbcJEOXPpIWSV_gEGQxmOpFWh6NwkWQ1gaKJq07CXwpkQHTRSItGfrbeydKsox0SLMQBk2ZEL_zx08n3h0hxzKXXI4SzWIba4YvDJmCnZCJaCRVbmDLrWOrZnNxdpmcL_hiMPjTjy4p1am--2dcyWNQhT7AFaNk_wPZ7kehAz4DvtACwtA-COOpu8Z0GXiAbq2q27KmLfSET7SubrCQe-GGU3NlGaCAeJrhj-IWA0a-f53MfGbgVRN85Poy9b6fsBGtc39qvI1s9DUh2t2vPiVg3R36oIeLoiP8XxWQyE-5GfVlvSyAmdaV7TsdfGiyfwQ9nwNFmwIzmNTejdWqKEtrN7QFmoEzEC2eCG2_z-fW7HiX9-wr6pGor-HTbMeh93XcY3qeBphyolgCjYb8NMraq7aSZzfQZDgsC3kWYZPyrO3H0DawpCdkN0J_Dr7U_5Z2ProAkzHWFQK7m2rStOLsH_tzb8mavSUo-_ZUYC1ULl6QZw1YdOLnfkkG1u2T5231DtqQ-QGZddZDN9ZDAVXaWg8tHN2yHuqth6L10G3reUUuP08vPp2xprgG06DyHJO5MKkKpciVCmyuQiNtOh5pjEUGSRdYUI5pIlMTSPi_gqiUIJ2FsEYYqUdWxq_Jjls7e0gofBEYEeQ2GoeJkmIc5wmMgp3BxFrq8IictGuT_fY5VDJ89sRF7CGT8iMi_Mp1wx6I35vHXviWPEXz9j61d2SnvKnse1CZpfpQm8JfquuIkg |
| linkProvider | Colorado Alliance of Research Libraries |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Enhancing+Robustness+and+Accuracy+in+Edge-Assisted+Visual+SLAM+Implementation&rft.jtitle=International+Journal+of+Networking+and+Computing&rft.au=Chenzhang+Xia&rft.au=Yuan+Wang&rft.au=Koji+Inoue&rft.date=2025&rft.pub=IJNC+Editorial+Committee&rft.issn=2185-2839&rft.eissn=2185-2847&rft.volume=15&rft.issue=2&rft.spage=85&rft.epage=101&rft_id=info:doi/10.15803%2Fijnc.15.2_85&rft.externalDocID=article_ijnc_15_2_15_85_article_char_en |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2185-2839&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2185-2839&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2185-2839&client=summon |