{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T01:11:15Z","timestamp":1752282675984,"version":"3.40.3"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031359941"},{"type":"electronic","value":"9783031359958"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-35995-8_46","type":"book-chapter","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T07:02:24Z","timestamp":1687935744000},"page":"653-667","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SLAM Methods for\u00a0Augmented Reality Systems for\u00a0Flight Simulators"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2706-6830","authenticated-orcid":false,"given":"Onyeka J.","family":"Nwobodo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1686-472X","authenticated-orcid":false,"given":"Kamil","family":"Wereszczy\u0144ski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1789-4939","authenticated-orcid":false,"given":"Krzysztof","family":"Cyran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"issue":"6","key":"46_CR1","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","volume":"32","author":"C Cadena","year":"2016","unstructured":"Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309\u20131332 (2016)","journal-title":"IEEE Trans. Robot."},{"key":"46_CR2","unstructured":"Chen, Y.: Algorithms for simultaneous localization and mapping, vol. 3, pp. 1\u201315, February 2013"},{"issue":"3","key":"46_CR3","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1109\/TIV.2017.2749181","volume":"2","author":"G Bresson","year":"2017","unstructured":"Bresson, G., Alsayed, Z., Yu, L., Glaser, S.: Simultaneous localization and mapping: a survey of current trends in autonomous driving. IEEE Trans. Intell. Veh. 2(3), 194\u2013220 (2017)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"46_CR4","unstructured":"Nava, Y.: Visual-LiDAR SLAM with loop closure. PhD thesis, KTH Royal Institute of Technology (2018)"},{"issue":"10","key":"46_CR5","doi-asserted-by":"publisher","first-page":"11728","DOI":"10.1109\/JSEN.2020.3022783","volume":"21","author":"T Sun","year":"2020","unstructured":"Sun, T., Liu, Y., Wang, Y., Xiao, Z.: An improved monocular visual-inertial navigation system. IEEE Sens. J. 21(10), 11728\u201311739 (2020)","journal-title":"IEEE Sens. J."},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: 2011 International Conference on Computer Vision, pp. 2320\u20132327. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126513"},{"key":"46_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1007\/978-3-319-10605-2_54","volume-title":"Computer Vision \u2013 ECCV 2014","author":"J Engel","year":"2014","unstructured":"Engel, J., Sch\u00f6ps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834\u2013849. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10605-2_54"},{"issue":"1\u20132","key":"46_CR8","first-page":"44","volume":"7","author":"R Berkvens","year":"2014","unstructured":"Berkvens, R., Vandermeulen, D., Vercauteren, C., Peremans, H., Weyn, M.: Feasibility of geomagnetic localization and geomagnetic RatSLAM. Int. J. Adv. Syst. Meas. 7(1\u20132), 44\u201356 (2014)","journal-title":"Int. J. Adv. Syst. Meas."},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., et al.: Kinectfusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 127\u2013136. IEEE (2011)","DOI":"10.1109\/ISMAR.2011.6092378"},{"issue":"7","key":"46_CR10","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.3390\/s18072118","volume":"18","author":"X Meng","year":"2018","unstructured":"Meng, X., Gao, W., Hu, Z.: Dense RGB-D SLAM with multiple cameras. Sensors 18(7), 2118 (2018)","journal-title":"Sensors"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Wang, S., Clark, R., Wen, H., Trigoni, N.: DeepVO: towards end-to-end visual odometry with deep recurrent convolutional neural networks. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2043\u20132050. IEEE (2017)","DOI":"10.1109\/ICRA.2017.7989236"},{"key":"46_CR12","doi-asserted-by":"publisher","first-page":"97466","DOI":"10.1109\/ACCESS.2019.2929133","volume":"7","author":"SA Mohamed","year":"2019","unstructured":"Mohamed, S.A., Haghbayan, M.-H., Westerlund, T., Heikkonen, J., Tenhunen, H., Plosila, J.: A survey on odometry for autonomous navigation systems. IEEE Access 7, 97466\u201397486 (2019)","journal-title":"IEEE Access"},{"key":"46_CR13","doi-asserted-by":"publisher","first-page":"149","DOI":"10.5194\/isprs-archives-XLII-2-W17-149-2019","volume":"42","author":"S Karam","year":"2019","unstructured":"Karam, S., Lehtola, V., Vosselman, G.: Integrating a low-cost mems imu into a laser-based slam for indoor mobile mapping. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 42, 149\u2013156 (2019)","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"46_CR14","unstructured":"R. K\u00fcmmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G$$^{2}$$o: a general framework for graph optimization. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3607\u20133613. IEEE (2011)"},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"Deschaud, J.-E.: IMLS-SLAM: scan-to-model matching based on 3D data. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2480\u20132485. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460653"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp. 2564\u20132571. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126544"},{"issue":"3","key":"46_CR17","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"H Bay","year":"2008","unstructured":"Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346\u2013359 (2008)","journal-title":"Comput. Vis. Image Underst."},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Chi, H.C., Tsai, T.H., Chen, S.Y.: Slam-based augmented reality system in interactive exhibition. In: 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), pp. 258\u2013262. IEEE (2020)","DOI":"10.1109\/ECICE50847.2020.9302012"},{"issue":"3","key":"46_CR19","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1162\/PRES_a_00264","volume":"25","author":"RT Azuma","year":"2016","unstructured":"Azuma, R.T.: The most important challenge facing augmented reality. Presence 25(3), 234\u2013238 (2016)","journal-title":"Presence"},{"key":"46_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Shu, M., Wang, Z., Wang, H., Wang, X.: A registration method for augmented reality system based on visual slam. In: 2019 International Conference on Electronic Engineering and Informatics (EEI), pp. 408\u2013411. IEEE (2019)","DOI":"10.1109\/EEI48997.2019.00094"},{"issue":"5","key":"46_CR21","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","volume":"31","author":"R Mur-Artal","year":"2015","unstructured":"Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147\u20131163 (2015)","journal-title":"IEEE Trans. Robot."},{"key":"46_CR22","doi-asserted-by":"crossref","unstructured":"Liu, H., Chen, M., Zhang, G., Bao, H., Bao, Y.: ICE-BA: incremental, consistent and efficient bundle adjustment for visual-inertial slam. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1974\u20131982 (2018)","DOI":"10.1109\/CVPR.2018.00211"},{"issue":"2","key":"46_CR23","first-page":"18","volume":"21","author":"J Cyrus","year":"2019","unstructured":"Cyrus, J., Krcmarik, D., Moezzi, R., Koci, J., Petru, M.: Hololens used for precise position tracking of the third party devices-autonomous vehicles. Commun.-Sci. Lett. Univ. Zilina 21(2), 18\u201323 (2019)","journal-title":"Commun.-Sci. Lett. Univ. Zilina"},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Hoffman, M.A.: Microsoft hololens development edition. Science 353(6302), 876\u2013876 (2016)","DOI":"10.1126\/science.aah5394"},{"issue":"6","key":"46_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2508363.2508374","volume":"32","author":"M Nie\u00dfner","year":"2013","unstructured":"Nie\u00dfner, M., Zollh\u00f6fer, M., Izadi, S., Stamminger, M.: Real-time 3D reconstruction at scale using voxel hashing. ACM Trans. Graph. (ToG) 32(6), 1\u201311 (2013)","journal-title":"ACM Trans. Graph. (ToG)"},{"issue":"5","key":"46_CR26","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1109\/TVCG.2014.2360403","volume":"21","author":"B Glocker","year":"2014","unstructured":"Glocker, B., Shotton, J., Criminisi, A., Izadi, S.: Real-time RGB-D camera relocalization via randomized ferns for keyframe encoding. IEEE Trans. Vis. Comput. Graph. 21(5), 571\u2013583 (2014)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"46_CR27","doi-asserted-by":"publisher","first-page":"7230","DOI":"10.3390\/s22197230","volume":"22","author":"P Skurowski","year":"2022","unstructured":"Skurowski, P., Nurzy\u0144ska, K., Pawlyta, M., Cyran, K.A.: Performance of QR code detectors near Nyquist limits. Sensors 22, 7230 (2022)","journal-title":"Sensors"},{"key":"46_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104992","volume":"114","author":"J Cheng","year":"2022","unstructured":"Cheng, J., Zhang, L., Chen, Q., Hu, X., Cai, J.: A review of visual slam methods for autonomous driving vehicles. Eng. Appl. Artif. Intell. 114, 104992 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"46_CR29","doi-asserted-by":"crossref","unstructured":"Juneja, A., Bhandari, L., Mohammadbagherpoor, H., Singh, A., Grant, E.: A comparative study of slam algorithms for indoor navigation of autonomous wheelchairs. In: 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS), pp. 261\u2013266. IEEE (2019)","DOI":"10.1109\/CBS46900.2019.9114512"},{"issue":"7","key":"46_CR30","doi-asserted-by":"publisher","first-page":"6907","DOI":"10.1109\/TITS.2021.3063477","volume":"23","author":"Q Zou","year":"2021","unstructured":"Zou, Q., Sun, Q., Chen, L., Nie, B., Li, Q.: A comparative analysis of lidar slam-based indoor navigation for autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 23(7), 6907\u20136921 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"46_CR31","doi-asserted-by":"crossref","unstructured":"Khan, M.U., Zaidi, S.A.A., Ishtiaq, A., Bukhari, S.U.R., Samer, S., Farman, A.: A comparative survey of lidar-slam and lidar based sensor technologies. In: 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/MAJICC53071.2021.9526266"},{"key":"46_CR32","doi-asserted-by":"publisher","unstructured":"Zhou, X., Huang, R.: A state-of-the-art review on SLAM. In: Intelligent Robotics and Applications. ICIRA 2022. LNCS, vol. 13457, pp. 240\u2013251. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-13835-5_22","DOI":"10.1007\/978-3-031-13835-5_22"},{"key":"46_CR33","doi-asserted-by":"crossref","unstructured":"Klose, S., Heise, P., Knoll, A.: Efficient compositional approaches for real-time robust direct visual odometry from RGB-D data. In: 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 1100\u20131106. IEEE (2013)","DOI":"10.1109\/IROS.2013.6696487"},{"key":"46_CR34","doi-asserted-by":"crossref","unstructured":"Gao, X., Wang, R., Demmel, N., Cremers, D.: LDSO: direct sparse odometry with loop closure. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2198\u20132204. IEEE (2018)","DOI":"10.1109\/IROS.2018.8593376"},{"issue":"1","key":"46_CR35","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1109\/TPAMI.2020.3010942","volume":"44","author":"W Dai","year":"2020","unstructured":"Dai, W., Zhang, Y., Li, P., Fang, Z., Scherer, S.: RGB-D SLAM in dynamic environments using point correlations. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 373\u2013389 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"22","key":"46_CR36","doi-asserted-by":"publisher","first-page":"4973","DOI":"10.3390\/s19224973","volume":"19","author":"D Kiss-Ill\u00e9s","year":"2019","unstructured":"Kiss-Ill\u00e9s, D., Barrado, C., Salam\u00ed, E.: GPS-SLAM: an augmentation of the ORB-SLAM algorithm. Sensors 19(22), 4973 (2019)","journal-title":"Sensors"},{"key":"46_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2020.165421","volume":"227","author":"L Cai","year":"2021","unstructured":"Cai, L., Ye, Y., Gao, X., Li, Z., Zhang, C.: An improved visual slam based on affine transformation for orb feature extraction. Optik 227, 165421 (2021)","journal-title":"Optik"},{"issue":"4","key":"46_CR38","doi-asserted-by":"publisher","first-page":"4076","DOI":"10.1109\/LRA.2018.2860039","volume":"3","author":"B Bescos","year":"2018","unstructured":"Bescos, B., F\u00e1cil, J.M., Civera, J., Neira, J.: DynaSLAM: tracking, mapping, and inpainting in dynamic scenes. IEEE Robot. Autom. Lett. 3(4), 4076\u20134083 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"12","key":"46_CR39","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1080\/01691864.2019.1610060","volume":"33","author":"J Cheng","year":"2019","unstructured":"Cheng, J., Sun, Y., Meng, M.Q.-H.: Improving monocular visual slam in dynamic environments: an optical-flow-based approach. Adv. Robot. 33(12), 576\u2013589 (2019)","journal-title":"Adv. Robot."},{"key":"46_CR40","doi-asserted-by":"crossref","unstructured":"Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semi-direct monocular visual odometry. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 15\u201322. IEEE (2014)","DOI":"10.1109\/ICRA.2014.6906584"},{"issue":"2","key":"46_CR41","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1109\/LRA.2017.2777002","volume":"3","author":"P Bergmann","year":"2017","unstructured":"Bergmann, P., Wang, R., Cremers, D.: Online photometric calibration of auto exposure video for realtime visual odometry and slam. IEEE Robot. Autom. Lett. 3(2), 627\u2013634 (2017)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"16","key":"46_CR42","doi-asserted-by":"publisher","first-page":"3604","DOI":"10.3390\/s19163604","volume":"19","author":"P Liu","year":"2019","unstructured":"Liu, P., Yuan, X., Zhang, C., Song, Y., Liu, C., Li, Z.: Real-time photometric calibrated monocular direct visual slam. Sensors 19(16), 3604 (2019)","journal-title":"Sensors"},{"issue":"4","key":"46_CR43","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","volume":"34","author":"T Qin","year":"2018","unstructured":"Qin, T., Li, P., Shen, S.: VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans. Robot. 34(4), 1004\u20131020 (2018)","journal-title":"IEEE Trans. Robot."},{"key":"46_CR44","doi-asserted-by":"crossref","unstructured":"Weiss, S., Achtelik, M.W., Lynen, S., Chli, M., Siegwart, R.: Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments. In: 2012 IEEE International Conference on Robotics and Automation, pp. 957\u2013964. IEEE (2012)","DOI":"10.1109\/ICRA.2012.6225147"},{"key":"46_CR45","doi-asserted-by":"crossref","unstructured":"Yin, H., Li, S., Tao, Y., Guo, J., Huang, B.: Dynam-SLAM: an accurate, robust stereo visual-inertial SLAM method in dynamic environments. IEEE Trans. Robot. (2022)","DOI":"10.1109\/TRO.2022.3199087"},{"key":"46_CR46","doi-asserted-by":"publisher","first-page":"137370","DOI":"10.1109\/ACCESS.2020.3012130","volume":"8","author":"Q Cheng","year":"2020","unstructured":"Cheng, Q., Zhang, S., Bo, S., Chen, D., Zhang, H.: Augmented reality dynamic image recognition technology based on deep learning algorithm. IEEE Access 8, 137370\u2013137384 (2020)","journal-title":"IEEE Access"},{"key":"46_CR47","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 (2014)"},{"key":"46_CR48","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"46_CR49","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"46_CR50","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1851\u20131858 (2017)","DOI":"10.1109\/CVPR.2017.700"},{"key":"46_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10514-015-9516-2","volume":"41","author":"X Gao","year":"2017","unstructured":"Gao, X., Zhang, T.: Unsupervised learning to detect loops using deep neural networks for visual SLAM system. Auton. Robot. 41, 1\u201318 (2017)","journal-title":"Auton. Robot."},{"key":"46_CR52","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s00034-019-01173-3","volume":"39","author":"M Geng","year":"2020","unstructured":"Geng, M., Shang, S., Ding, B., Wang, H., Zhang, P.: Unsupervised learning-based depth estimation-aided visual slam approach. Circuits Syst. Signal Process. 39, 543\u2013570 (2020)","journal-title":"Circuits Syst. Signal Process."},{"key":"46_CR53","doi-asserted-by":"publisher","first-page":"25442","DOI":"10.1109\/ACCESS.2020.2970238","volume":"8","author":"F Li","year":"2020","unstructured":"Li, F., et al.: A mobile robot visual slam system with enhanced semantics segmentation. IEEE Access 8, 25442\u201325458 (2020)","journal-title":"IEEE Access"},{"key":"46_CR54","doi-asserted-by":"publisher","first-page":"75545","DOI":"10.1109\/ACCESS.2018.2873617","volume":"6","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Wei, L., Shen, P., Wei, W., Zhu, G., Song, J.: Semantic SLAM based on object detection and improved octomap. IEEE Access 6, 75545\u201375559 (2018)","journal-title":"IEEE Access"},{"key":"46_CR55","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement, arXiv preprint arXiv:1804.02767 (2018)"},{"key":"46_CR56","doi-asserted-by":"crossref","unstructured":"Tateno, K., Tombari, F., Laina, I., Navab, N.: CNN-SLAM: real-time dense monocular slam with learned depth prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6243\u20136252 (2017)","DOI":"10.1109\/CVPR.2017.695"},{"key":"46_CR57","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, S., Long, Z., Gu, D.: UnDeepVO: monocular visual odometry through unsupervised deep learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 7286\u20137291. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8461251"},{"key":"46_CR58","unstructured":"Vijayanarasimhan, S., Ricco, S., Schmid, C., Sukthankar, R., Fragkiadaki, K.: SFM-NET: learning of structure and motion from video, arXiv preprint arXiv:1704.07804 (2017)"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35995-8_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T14:08:00Z","timestamp":1691071680000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35995-8_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031359941","9783031359958"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35995-8_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"530","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"188","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}