{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:49:26Z","timestamp":1777654166478,"version":"3.51.4"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200465","type":"print"},{"value":"9783031200472","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20047-2_32","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T10:02:55Z","timestamp":1666432975000},"page":"552-570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Learned Monocular Depth Priors in\u00a0Visual-Inertial Initialization"],"prefix":"10.1007","author":[{"given":"Yunwen","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Abhishek","family":"Kar","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Turner","sequence":"additional","affiliation":[]},{"given":"Adarsh","family":"Kowdle","sequence":"additional","affiliation":[]},{"given":"Chao X.","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Ryan C.","family":"DuToit","sequence":"additional","affiliation":[]},{"given":"Konstantine","family":"Tsotsos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"32_CR1","unstructured":"Agarwal, S., Mierle, K., Others: Ceres solver. https:\/\/ceres-solver.org"},{"key":"32_CR2","unstructured":"Almalioglu, Y., et al.: SelfVIO: self-supervised deep monocular visual-inertial odometry and depth estimation. CoRR abs\/1911.09968 (2019). https:\/\/doi.org\/arxiv.org\/abs\/1911.09968"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Barron, J.T.: A general and adaptive robust loss function. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4331\u20134339 (2019)","DOI":"10.1109\/CVPR.2019.00446"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Bloesch, M., Czarnowski, J., Clark, R., Leutenegger, S., Davison, A.J.: CodeSLAM-learning a compact, optimisable representation for dense visual slam. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2560\u20132568 (2018)","DOI":"10.1109\/CVPR.2018.00271"},{"issue":"10","key":"32_CR5","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1177\/0278364915620033","volume":"35","author":"M Burru","year":"2016","unstructured":"Burru, M., et al.: The EuRoC micro aerial vehicle datasets. Int. J. Robot. Res. 35(10), 1157\u20131163 (2016)","journal-title":"Int. J. Robot. Res."},{"issue":"6","key":"32_CR6","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","volume":"37","author":"C Campos","year":"2021","unstructured":"Campos, C., Elvira, R., Rodr\u00edguez, J.J.G., Montiel, J.M., Tard\u00f3s, J.D.: ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap slam. IEEE Trans. Robot. 37(6), 1874\u20131890 (2021)","journal-title":"IEEE Trans. Robot."},{"key":"32_CR7","unstructured":"Campos, C., Montiel, J.M.M., Tard\u00f3s, J.D.: Fast and robust initialization for visual-inertial SLAM. CoRR abs\/1908.10653 (2019), https:\/\/doi.org\/arxiv.org\/abs\/1908.10653"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Campos, C., Montiel, J.M., Tard\u00f3s, J.D.: Inertial-only optimization for visual-inertial initialization. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 51\u201357. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9197334"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Chen, C., Lu, X., Markham, A., Trigoni, N.: IONet: learning to cure the curse of drift in inertial odometry. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12102"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Chen, C., et al.: Selective sensor fusion for neural visual-inertial odometry. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10542\u201310551 (2019)","DOI":"10.1109\/CVPR.2019.01079"},{"issue":"5","key":"32_CR11","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/TRO.2008.2003276","volume":"24","author":"J Civera","year":"2008","unstructured":"Civera, J., Davison, A.J., Montiel, J.M.: Inverse depth parametrization for monocular slam. IEEE Trans. Rob. 24(5), 932\u2013945 (2008)","journal-title":"IEEE Trans. Rob."},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Clark, R., Wang, S., Wen, H., Markham, A., Trigoni, N.: ViNet: visual-inertial odometry as a sequence-to-sequence learning problem. In: Proceedings of the AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11215"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Concha, A., Civera, J.: RGBDTAM: A cost-effective and accurate RGB-D tracking and mapping system. CoRR abs\/1703.00754 (2017). https:\/\/doi.org\/arxiv.org\/abs\/1703.00754","DOI":"10.1109\/IROS.2017.8206593"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: ScanNet: richly-annotated 3d reconstructions of indoor scenes. In: Proceedings Computer Vision and Pattern Recognition (CVPR). IEEE (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Du, R., et al.: DepthLab: real-time 3D interaction with depth maps for mobile augmented reality. In: Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, pp. 829\u2013843 (2020)","DOI":"10.1145\/3379337.3415881"},{"key":"32_CR16","unstructured":"Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. CoRR abs\/1406.2283 (2014). https:\/\/doi.org\/arxiv.org\/abs\/1406.2283"},{"issue":"1","key":"32_CR17","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1109\/TRO.2013.2279412","volume":"30","author":"F Endres","year":"2013","unstructured":"Endres, F., Hess, J., Sturm, J., Cremers, D., Burgard, W.: 3-D mapping with an RGB-D camera. IEEE Trans. Rob. 30(1), 177\u2013187 (2013)","journal-title":"IEEE Trans. Rob."},{"key":"32_CR18","unstructured":"Fei, X., Soatto, S.: Xivo: an open-source software for visual-inertial odometry (2019). https:\/\/doi.org\/github.com\/ucla-vision\/xivo"},{"key":"32_CR19","unstructured":"Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: On-manifold preintegration theory for fast and accurate visual-inertial navigation. CoRR abs\/1512.02363 (2015). https:\/\/doi.org\/arxiv.org\/abs\/1512.02363"},{"key":"32_CR20","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"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Garg, R., Wadhwa, N., Ansari, S., Barron, J.T.: Learning single camera depth estimation using dual-pixels. CoRR abs\/1904.05822 (2019). https:\/\/doi.org\/arxiv.org\/abs\/1904.05822","DOI":"10.1109\/ICCV.2019.00772"},{"issue":"11","key":"32_CR22","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Rob. Res. (IJRR) 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Rob. Res. (IJRR)"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Geneva, P., Eckenhoff, K., Lee, W., Yang, Y., Huang, G.: OpenVINS: a research platform for visual-inertial estimation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4666\u20134672. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9196524"},{"key":"32_CR24","unstructured":"Guennebaud, G., Jacob, B., et al.: Eigen v3 (2010). https:\/\/eigen.tuxfamily.org"},{"key":"32_CR25","doi-asserted-by":"publisher","unstructured":"Guo, C.X., Roumeliotis, S.I.: IMU-RGBD camera 3D pose estimation and extrinsic calibration: observability analysis and consistency improvement. In: 2013 IEEE International Conference on Robotics and Automation, pp. 2935\u20132942 (2013). https:\/\/doi.org\/10.1109\/ICRA.2013.6630984","DOI":"10.1109\/ICRA.2013.6630984"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Han, L., Lin, Y., Du, G., Lian, S.: DeepVIO: self-supervised deep learning of monocular visual inertial odometry using 3D geometric constraints. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6906\u20136913. IEEE (2019)","DOI":"10.1109\/IROS40897.2019.8968467"},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, ISBN: 0521540518 (2004)","DOI":"10.1017\/CBO9780511811685"},{"key":"32_CR28","doi-asserted-by":"publisher","unstructured":"Herath, S., Yan, H., Furukawa, Y.: RoNIN: robust neural inertial navigation in the wild: benchmark, evaluations, and new methods. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 3146\u20133152 (2020). https:\/\/doi.org\/10.1109\/ICRA40945.2020.9196860","DOI":"10.1109\/ICRA40945.2020.9196860"},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Hernandez, J., Tsotsos, K., Soatto, S.: Observability, identifiability and sensitivity of vision-aided inertial navigation. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2319\u20132325. IEEE (2015)","DOI":"10.1109\/ICRA.2015.7139507"},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Huai, Z., Huang, G.: Robocentric visual-inertial odometry. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6319\u20136326. IEEE (2018)","DOI":"10.1109\/IROS.2018.8593643"},{"key":"32_CR31","doi-asserted-by":"publisher","unstructured":"Huang, G.: Visual-inertial navigation: a concise review. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 9572\u20139582 (2019). https:\/\/doi.org\/10.1109\/ICRA.2019.8793604","DOI":"10.1109\/ICRA.2019.8793604"},{"key":"32_CR32","doi-asserted-by":"publisher","unstructured":"Huber, P.J.: Robust estimation of a location parameter. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in statistics, pp. 492\u2013518. Springer, New York (1992). https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_35","DOI":"10.1007\/978-1-4612-4380-9_35"},{"key":"32_CR33","unstructured":"Jones, E., Vedaldi, A., Soatto, S.: Inertial structure from motion with autocalibration. In: Workshop on Dynamical Vision, vol. 25, p. 11 (2007)"},{"issue":"1","key":"32_CR34","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/LRA.2016.2521413","volume":"2","author":"J Kaiser","year":"2017","unstructured":"Kaiser, J., Martinelli, A., Fontana, F., Scaramuzza, D.: Simultaneous state initialization and gyroscope bias calibration in visual inertial aided navigation. IEEE Rob. Autom. Lett. 2(1), 18\u201325 (2017). https:\/\/doi.org\/10.1109\/LRA.2016.2521413","journal-title":"IEEE Rob. Autom. Lett."},{"issue":"1","key":"32_CR35","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1177\/0278364910382802","volume":"30","author":"J Kelly","year":"2011","unstructured":"Kelly, J., Sukhatme, G.S.: Visual-inertial sensor fusion: localization, mapping and sensor-to-sensor self-calibration. Int. J. Rob. Res. 30(1), 56\u201379 (2011)","journal-title":"Int. J. Rob. Res."},{"key":"32_CR36","doi-asserted-by":"crossref","unstructured":"Kopf, J., Rong, X., Huang, J.B.: Robust consistent video depth estimation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00166"},{"key":"32_CR37","unstructured":"Krasin, I., et al.: OpenImages: a public dataset for large-scale multi-label and multi-class image classification. Dataset available from https:\/\/storage.googleapis.com\/openimages\/web\/index.html (2017)"},{"issue":"2","key":"32_CR38","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","volume":"81","author":"V Lepetit","year":"2009","unstructured":"Lepetit, V., Moreno-Noguer, F., Fua, P.: EPNP: an accurate o(n) solution to the PNP problem. Int. J. Computer Vis. 81(2), 155 (2009)","journal-title":"Int. J. Computer Vis."},{"issue":"3","key":"32_CR39","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1177\/0278364914554813","volume":"34","author":"S Leutenegger","year":"2015","unstructured":"Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual-inertial odometry using nonlinear optimization. Int. J. Rob. Res. 34(3), 314\u2013334 (2015)","journal-title":"Int. J. Rob. Res."},{"issue":"3","key":"32_CR40","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1177\/0278364914554813","volume":"34","author":"S Leutenegger","year":"2015","unstructured":"Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual-inertial odometry using nonlinear optimization. Int. J. Rob. Res. 34(3), 314\u2013334 (2015)","journal-title":"Int. J. Rob. Res."},{"key":"32_CR41","doi-asserted-by":"crossref","unstructured":"Li, C., Waslander, S.L.: Towards end-to-end learning of visual inertial odometry with an EKF. In: 2020 17th Conference on Computer and Robot Vision (CRV), pp. 190\u2013197. IEEE (2020)","DOI":"10.1109\/CRV50864.2020.00033"},{"key":"32_CR42","doi-asserted-by":"publisher","unstructured":"Li, J., Bao, H., Zhang, G.: Rapid and robust monocular visual-inertial initialization with gravity estimation via vertical edges. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6230\u20136236 (2019). https:\/\/doi.org\/10.1109\/IROS40897.2019.8968456","DOI":"10.1109\/IROS40897.2019.8968456"},{"key":"32_CR43","unstructured":"Li, M., Mourikis, A.I.: A convex formulation for motion estimation using visual and inertial sensors. In: Proceedings of the Workshop on Multi-View Geometry, held in conjunction with RSS. Berkeley, CA, July 2014"},{"issue":"6","key":"32_CR44","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1177\/0278364913481251","volume":"32","author":"M Li","year":"2013","unstructured":"Li, M., Mourikis, A.I.: High-precision, consistent EKF-based visual-inertial odometry. Int. J. Rob. Res. 32(6), 690\u2013711 (2013)","journal-title":"Int. J. Rob. Res."},{"key":"32_CR45","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Learning the depths of moving people by watching frozen people. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00465"},{"issue":"4","key":"32_CR46","doi-asserted-by":"publisher","first-page":"5653","DOI":"10.1109\/LRA.2020.3007421","volume":"5","author":"W Liu","year":"2020","unstructured":"Liu, W., et al.: TLIO: tight learned inertial odometry. IEEE Rob. Autom. Lett. 5(4), 5653\u20135660 (2020)","journal-title":"IEEE Rob. Autom. Lett."},{"issue":"2","key":"32_CR47","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/s11263-013-0647-7","volume":"106","author":"A Martinelli","year":"2014","unstructured":"Martinelli, A.: Closed-form solution of visual-inertial structure from motion. Int. J. Comput. Vision 106(2), 138\u2013152 (2014)","journal-title":"Int. J. Comput. Vision"},{"issue":"5","key":"32_CR48","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","volume":"33","author":"R Mur-Artal","year":"2017","unstructured":"Mur-Artal, R., Tard\u00f3s, J.D.: ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans. Rob. 33(5), 1255\u20131262 (2017). https:\/\/doi.org\/10.1109\/TRO.2017.2705103","journal-title":"IEEE Trans. Rob."},{"key":"32_CR49","unstructured":"Qin, T., Li, P., Shen, S.: VINS-Mono: a robust and versatile monocular visual-inertial state estimator. CoRR abs\/1708.03852 (2017). https:\/\/doi.org\/arxiv.org\/abs\/1708.03852"},{"key":"32_CR50","doi-asserted-by":"publisher","unstructured":"Qin, T., Shen, S.: Robust initialization of monocular visual-inertial estimation on aerial robots. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4225\u20134232 (2017). https:\/\/doi.org\/10.1109\/IROS.2017.8206284","DOI":"10.1109\/IROS.2017.8206284"},{"key":"32_CR51","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. ArXiv preprint (2021)","DOI":"10.1109\/ICCV48922.2021.01196"},{"issue":"3","key":"32_CR52","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","volume":"44","author":"R Ranftl","year":"2020","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 44(3), 1623\u20131637 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"issue":"4","key":"32_CR53","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MRA.2011.943233","volume":"18","author":"D Scaramuzza","year":"2011","unstructured":"Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. IEEE Rob. Autom. Mag. 18(4), 80\u201392 (2011). https:\/\/doi.org\/10.1109\/MRA.2011.943233","journal-title":"IEEE Rob. Autom. Mag."},{"key":"32_CR54","unstructured":"Tang, C., Tan, P.: BA-Net: dense bundle adjustment networks. In: International Conference on Learning Representations (2018)"},{"key":"32_CR55","doi-asserted-by":"publisher","unstructured":"Troiani, C., Martinelli, A., Laugier, C., Scaramuzza, D.: 2-point-based outlier rejection for camera-IMU systems with applications to micro aerial vehicles. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5530\u20135536 (2014). https:\/\/doi.org\/10.1109\/ICRA.2014.6907672","DOI":"10.1109\/ICRA.2014.6907672"},{"key":"32_CR56","doi-asserted-by":"crossref","unstructured":"Tsotsos, K., Chiuso, A., Soatto, S.: Robust inference for visual-inertial sensor fusion. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5203\u20135210. IEEE (2015)","DOI":"10.1109\/ICRA.2015.7139924"},{"key":"32_CR57","doi-asserted-by":"crossref","unstructured":"Von Stumberg, L., Usenko, V., Cremers, D.: Direct sparse visual-inertial odometry using dynamic marginalization. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2510\u20132517. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8462905"},{"key":"32_CR58","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":"32_CR59","doi-asserted-by":"crossref","unstructured":"Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: Elasticfusion: dense slam without a pose graph. In: Robotics: Science and Systems (2015)","DOI":"10.15607\/RSS.2015.XI.001"},{"key":"32_CR60","doi-asserted-by":"crossref","unstructured":"Wu, K.J., Guo, C.X., Georgiou, G., Roumeliotis, S.I.: Vins on wheels. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5155\u20135162. IEEE (2017)","DOI":"10.1109\/ICRA.2017.7989603"},{"key":"32_CR61","doi-asserted-by":"crossref","unstructured":"Zuo, X., Merrill, N., Li, W., Liu, Y., Pollefeys, M., Huang, G.: Codevio: visual-inertial odometry with learned optimizable dense depth. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 14382\u201314388. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9560792"},{"issue":"3","key":"32_CR62","doi-asserted-by":"publisher","first-page":"6116","DOI":"10.1109\/LRA.2021.3091407","volume":"6","author":"D Zu\u00f1iga-No\u00ebl","year":"2021","unstructured":"Zu\u00f1iga-No\u00ebl, D., Moreno, F.A., Gonzalez-Jimenez, J.: An analytical solution to the IMU initialization problem for visual-inertial systems. IEEE Rob. Autom. Lett. 6(3), 6116\u20136122 (2021). https:\/\/doi.org\/10.1109\/LRA.2021.3091407","journal-title":"IEEE Rob. Autom. Lett."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20047-2_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T23:14:33Z","timestamp":1666739673000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20047-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200465","9783031200472"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20047-2_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"0","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}