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However, transforming these passive video feeds into active guidance systems requires accurate camera calibration, a process that is traditionally labor-intensive and unscalable in large facilities. This paper introduces a novel, automated framework that leverages a mobile quadruped robot (Boston Dynamics Spot) as a dynamic calibration agent. We propose a single-pass approach with two planar calibration algorithms, which mines unstructured robotic trajectories to construct distinct geometric features on the ground plane. By synthesizing \u201cvirtual rectangles\u201d from the robot\u2019s odometry, our method first recovers camera focal length through vanishing point estimation by constructing virtual rectangles, and then solves for extrinsic 6-DoF pose using two algorithms: our proposed Virtual Rectangle (ViR) algorithm and a standard planar Perspective-n-Point (PnP) algorithm. Experimental validation using real-world data demonstrates the system\u2019s robustness to sensor noise, maintaining focal length errors around 5%, rotational errors around 5\u00b0 and relative translation errors of approximately 10%, while synthetic simulations indicate focal length errors generally under 10%, rotational errors under 1.5\u00b0, and translation errors also around 10% despite heavy image and object disturbances. This work eliminates the need for manual calibration targets for dynamic camera calibration, effectively converting static security infrastructure into a metric sensing network capable of supporting high-fidelity social robotics applications.<\/jats:p>","DOI":"10.3390\/s26175473","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:55:56Z","timestamp":1788155756000},"page":"5473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Single-Pass Approach That Mines Unstructured Robotic Trajectories for Calibrating Surveillance Cameras"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4148-6656","authenticated-orcid":false,"given":"Wayne","family":"Lam","sequence":"first","affiliation":[{"name":"Department of Computer Science, The Graduate Center\u2014CUNY, New York, NY 10016, 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J. Disaster Risk Reduct."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1177\/003335490712200319","article-title":"Identifying and protecting vulnerable populations in public health emergencies: Addressing gaps in education and training","volume":"122","author":"Wingate","year":"2007","journal-title":"Public Health Rep."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xu, Z., Jian, Z., Tang, G., Yangli, Y., Xiao, A., Wang, X., and Liang, B. (2023). Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA48891.2023.10160854"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sivacoumare, A., S, S.M., Satheesh, S., Athul, T., V, M., and Vinopraba, T. (2021, January 13\u201316). AI Quadruped Robot Assistant for the Visually Impaired. Proceedings of the IECON 2021\u201447th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada.","DOI":"10.1109\/IECON48115.2021.9589508"},{"key":"ref_5","unstructured":"Boston Dynamics (2025, January 29). Spot SDK\u2014Autowalk Services. Available online: https:\/\/dev.bostondynamics.com\/docs\/concepts\/autonomy\/autowalk_service."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1007\/s11263-014-0725-5","article-title":"Infinitesimal Plane-Based Pose Estimation","volume":"109","author":"Collins","year":"2014","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","article-title":"EPnP: An Accurate O(n) Solution to the PnP Problem","volume":"81","author":"Lepetit","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yang, J., Larsson, V., Olsson, C., and \u00c5str\u00f6m, K. (2023). Revisiting the P3P Problem. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.00472"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vuong, K., Tamburo, R., and Narasimhan, S.G. (2024). Toward Planet-Wide Traffic Camera Calibration. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV57701.2024.00836"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sugimoto, M., Shimada, S., and Hashizume, H. (2022). RefRec+: Six Degree-of-Freedom Estimation for Smartphone Using Floor Reflecting Light. Front. Comput. Sci., 4.","DOI":"10.3389\/fcomp.2022.856942"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bartl, V., Jur\u00e1nek, R., \u0160pa\u0148hel, J., and Herout, A. (2020). PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane. Proceedings of the 2020 Digital Image Computing: Techniques and Applications (DICTA), IEEE.","DOI":"10.1109\/DICTA51227.2020.9363417"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Olson, E. (2011). AprilTag: A robust and flexible visual fiducial system. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ICRA.2011.5979561"},{"key":"ref_14","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023). Ultralytics YOLOv8, Ultralytics."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A Method for Registration of 3-D Shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0262-8856(92)90066-C","article-title":"Object Modelling by Registration of Multiple Range Images","volume":"10","author":"Chen","year":"1992","journal-title":"Image Vis. Comput."},{"key":"ref_17","unstructured":"Segal, A., Haehnel, D., and Thrun, S. (July, January 28). Generalized-ICP. Proceedings of the Robotics: Science and Systems (RSS), Seattle, WA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 21\u201325). LOAM: Lidar Odometry and Mapping in Real-time. Proceedings of the Robotics: Science and Systems (RSS), Berkeley, CA, USA.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018). LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, C., Chen, C.L., and Xie, L. (2021). F-LOAM: Fast LiDAR Odometry and Mapping. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS51168.2021.9636655"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ye, H., Chen, Y., and Liu, M. (2019). Tightly Coupled 3D Lidar Inertial Odometry and Mapping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA.2019.8793511"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qin, C., Ye, H., Pranata, C.E., Han, J., Zhang, S., and Liu, M. (2020). LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA40945.2020.9197567"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/LRA.2021.3064227","article-title":"FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1109\/TRO.2022.3141876","article-title":"FAST-LIO2: Fast Direct LiDAR-Inertial Odometry","volume":"38","author":"Xu","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1109\/LRA.2022.3142739","article-title":"Direct LiDAR Odometry: Fast Localization with Dense Point Clouds","volume":"7","author":"Chen","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, K., Nemiroff, R., and Lopez, B.T. (2023). Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA48891.2023.10160508"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2200459","DOI":"10.1002\/aisy.202200459","article-title":"Point-LIO: Robust High-Bandwidth Light Detection and Ranging Inertial Odometry","volume":"5","author":"He","year":"2023","journal-title":"Adv. Intell. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4861","DOI":"10.1109\/LRA.2022.3152830","article-title":"Faster-LIO: Lightweight Tightly Coupled LiDAR-Inertial Odometry Using Parallel Sparse Incremental Voxels","volume":"7","author":"Bai","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8518","DOI":"10.1109\/LRA.2022.3187250","article-title":"Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry","volume":"7","author":"Yuan","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3905","DOI":"10.1109\/TMM.2022.3168423","article-title":"D-LIOM: Tightly-coupled direct LiDAR-inertial odometry and mapping","volume":"25","author":"Wang","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_31","first-page":"134580E","article-title":"Real-time computer vision and deep learning for 3D environment modeling, camera network calibration, and human-robot interaction using a robot dog","volume":"Volume 13458","author":"Kehtarnavaz","year":"2025","journal-title":"Proceedings of the Real-Time Image Processing and Deep Learning 2025"},{"key":"ref_32","unstructured":"APRIL Robotics Laboratory (2025, January 29). AprilTag\u2014APRIL Robotics lab\u2014University of Michigan. Available online: https:\/\/april.eecs.umich.edu\/software\/apriltag."},{"key":"ref_33","unstructured":"Logitech (2025, July 15). Logitech HD Webcam C270 Technical Specifications. Available online: https:\/\/support.logi.com\/hc\/en-us\/articles\/360023462093-Logitech-HD-Webcam-C270-Technical-Specifications."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_35","unstructured":"Unity Technologies (2025). Unity, Unity Technologies. version 2022.3.x."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5473\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T13:55:07Z","timestamp":1788530107000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5473"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,29]]},"references-count":35,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175473"],"URL":"https:\/\/doi.org\/10.3390\/s26175473","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,29]]}}}