{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T12:47:17Z","timestamp":1778071637541,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001824","name":"Czech Science Foundation","doi-asserted-by":"publisher","award":["20-27034J"],"award-info":[{"award-number":["20-27034J"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual teach and repeat navigation (VT&amp;R) is popular in robotics thanks to its simplicity and versatility. It enables mobile robots equipped with a camera to traverse learned paths without the need to create globally consistent metric maps. Although teach and repeat frameworks have been reported to be relatively robust to changing environments, they still struggle with day-to-night and seasonal changes. This paper aims to find the horizontal displacement between prerecorded and currently perceived images required to steer a robot towards the previously traversed path. We employ a fully convolutional neural network to obtain dense representations of the images that are robust to changes in the environment and variations in illumination. The proposed model achieves state-of-the-art performance on multiple datasets with seasonal and day\/night variations. In addition, our experiments show that it is possible to use the model to generate additional training examples that can be used to further improve the original model\u2019s robustness. We also conducted a real-world experiment on a mobile robot to demonstrate the suitability of our method for VT&amp;R.<\/jats:p>","DOI":"10.3390\/s22082975","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T23:07:16Z","timestamp":1649891236000},"page":"2975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Contrastive Learning for Image Registration in Visual Teach and Repeat Navigation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1791-447X","authenticated-orcid":false,"given":"Zden\u011bk","family":"Rozsyp\u00e1lek","sequence":"first","affiliation":[{"name":"Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0071-5834","authenticated-orcid":false,"given":"George","family":"Broughton","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6820-4013","authenticated-orcid":false,"given":"Pavel","family":"Linder","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3598-1630","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Rou\u010dek","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4900-1085","authenticated-orcid":false,"given":"Jan","family":"Blaha","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8437-1711","authenticated-orcid":false,"given":"Leonard","family":"Mentzl","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7464-3784","authenticated-orcid":false,"given":"Keerthy","family":"Kusumam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Nottingham, Jubilee Campus, 7301 Wollaton Rd, Lenton, Nottingham NG8 1BB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-7916","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Krajn\u00edk","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Debeunne, C., and Vivet, D. (2020). A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping. Sensors, 20.","DOI":"10.3390\/s20072068"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1109\/LRA.2021.3061377","article-title":"Mobile Manipulator for Autonomous Localization, Grasping and Precise Placement of Construction Material in a Semi-structured Environment","volume":"6","author":"Broughton","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_3","unstructured":"Thrun, S., Burgard, W., and Fox, D. (2010). Probabilistic Robotics, MIT Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Tran. Robot."},{"key":"ref_5","first-page":"1","article-title":"FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments","volume":"33","author":"Fentanes","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/MRA.2016.2636359","article-title":"The strands project: Long-term autonomy in everyday environments","volume":"24","author":"Hawes","year":"2017","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s11263-020-01399-8","article-title":"Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis","volume":"129","author":"Zhang","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rosen, D.M., Mason, J., and Leonard, J.J. (2016, January 16\u201321). Towards lifelong feature-based mapping in semi-static environments. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487237"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lowe, D. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, ICCV, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hoffer, E., and Ailon, N. (2015). Deep Metric Learning Using Triplet Network. Similarity-Based Pattern Recognition Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., and Makedon, F. (2020). A Survey on Contrastive Self-Supervised Learning. Technologies, 9.","DOI":"10.3390\/technologies9010002"},{"key":"ref_15","first-page":"127","article-title":"Image features for visual teach-and-repeat navigation in changing environments","volume":"88","author":"Kusumam","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1002\/rob.21655","article-title":"Robust Monocular Visual Teach and Repeat Aided by Local Ground Planarity and Color-constant Imagery","volume":"34","author":"Clement","year":"2016","journal-title":"J. Field Robot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1002\/rob.20342","article-title":"Visual teach and repeat for long-range rover autonomy","volume":"27","author":"Furgale","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). BRIEF: Binary robust independent elementary features. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15561-1_56"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Birchfield, S.T. (2010). Vision-Based Path Following without Calibration. Mob. Robot. Navig., 427\u2013446.","DOI":"10.5772\/8981"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/TRO.2009.2017140","article-title":"Qualitative Vision-Based Path Following","volume":"25","author":"Chen","year":"2009","journal-title":"IEEE Trans. Robot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1002\/rob.20354","article-title":"Simple yet stable bearing-only navigation","volume":"27","author":"Faigl","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Krajn\u00edk, T., Majer, F., Halodov\u00e1, L., and Vintr, T. (2018, January 1\u20135). Navigation without localisation: Reliable teach and repeat based on the convergence theorem. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593803"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dall\u2019Osto, D., Fischer, T., and Milford, M. (October, January 27). Fast and robust bio-inspired teach and repeat navigation. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636334"},{"key":"ref_24","unstructured":"Thrun, S. (1994, January 12\u201316). A Lifelong Learning Perspective for Mobile Robot Control. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Munich, Germany."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1177\/0278364913499193","article-title":"Experience-based navigation for long-term localisation","volume":"32","author":"Churchill","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dayoub, F., and Duckett, T. (2008, January 22\u201326). An adaptive appearance-based map for long-term topological localization of mobile robots. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650701"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Halodov\u00e1, L., Dvo\u0159\u00e1kov\u00e1, E., Majer, F., Vintr, T., Mozos, O.M., Dayoub, F., and Krajn\u00edk, T. (2019, January 3\u20138). Predictive and adaptive maps for long-term visual navigation in changing environments. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967994"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TSMC.2020.3018325","article-title":"Deep Learning in Robotics: Survey on Model Structures and Training Strategies","volume":"51","author":"Karoly","year":"2021","journal-title":"IEEE Trans. Syst. Man, Cybern. Syst."},{"key":"ref_29","first-page":"18661","article-title":"Supervised Contrastive Learning","volume":"33","author":"Larochelle","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.robot.2014.08.005","article-title":"Superpixel-based appearance change prediction for long-term navigation across seasons","volume":"69","author":"Neubert","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_31","unstructured":"Sunderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., and Milford, M. (October, January 28). On the performance of ConvNet features for place recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Suenderhauf, N., Shirazi, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., and Milford, M. (2015, January 13\u201317). Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free. Proceedings of the Robotics: Science and Systems, Rome, Italy.","DOI":"10.15607\/RSS.2015.XI.022"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TRO.2015.2496823","article-title":"Visual Place Recognition: A Survey","volume":"32","author":"Lowry","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-End Object Detection with Transformers, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_36","first-page":"10096","article-title":"EfficientNetV2: Smaller Models and Faster Training","volume":"139","author":"Tan","year":"2021","journal-title":"ICML"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Guo, D., Wang, J., Cui, Y., Wang, Z., and Chen, S. (2020, January 13\u201319). SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ichida, A.Y., Meneguzzi, F., and Ruiz, D.D. (2018, January 8\u201313). Measuring Semantic Similarity between Sentences Using A Siamese Neural Network. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489433"},{"key":"ref_39","unstructured":"Daum\u00e9, H., and Singh, A. (2020, January 13\u201318). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_40","unstructured":"Chopra, S., Hadsell, R., and Lecun, Y. (2005, January 20\u201326). Learning a Similarity Metric Discriminatively, with Application to Face Verification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05), San Diego, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., and Shah, R. (1994). Signature Verification Using A \u201cSiamese\u201d Time Delay Neural Network. Series in Machine Perception and Artificial Intelligence Advances in Pattern Recognition Systems Using Neural Network Technologies, World Scientific Publishing Ltd.","DOI":"10.1142\/9789812797926_0003"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Spencer, J., Bowden, R., and Hadfield, S. (2020, January 13\u201319). Same features, different day: Weakly supervised feature learning for seasonal invariance. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00649"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Broughton, G., Linder, P., Rou\u010dek, T., Vintr, T., and Krajn\u00edk, T. (September, January 31). Robust Image Alignment for Outdoor Teach-and-Repeat Navigation. Proceedings of the 2021 European Conference on Mobile Robots (ECMR), Bonn, Germany.","DOI":"10.1109\/ECMR50962.2021.9568832"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rozsypalek, Z., Broughton, G., Linder, P., Roucek, T., Kusumam, K., and Krajnik, T. (2022, January 25\u201329). Semi-Supervised Learning for Image Alignment in Teach and Repeat navigation. Proceedings of the Symposium on Applied Computing (SAC), Brno, Czech Republic.","DOI":"10.1145\/3477314.3507045"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cen, M., and Jung, C. (2018, January 7\u201310). Fully Convolutional Siamese Fusion Networks for Object Tracking. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451102"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, L., Jiang, P., Wang, F., and Wang, X. (December, January 29). Robust Real-Time Visual Object Tracking via Multi-Scale Fully Convolutional Siamese Networks. Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, NSW, Australia.","DOI":"10.1109\/DICTA.2017.8227487"},{"key":"ref_47","unstructured":"Corporation, N.B. (2022, April 10). Nordlandsbanen: Minute by Minute, Season by Season. 15 January 2013. Available online: https:\/\/nrkbeta.no\/2013\/01\/15\/nordlandsbanen-minute-by-minute-season-by-season\/."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yan, Z., Sun, L., Krajnik, T., and Ruichek, Y. (2020, January 25\u201329). EU Long-term Dataset with Multiple Sensors for Autonomous Driving. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341406"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fox, D., Thrun, S., Burgard, W., and Dellaert, F. (2001). Particle Filters for Mobile Robot Localization. Sequential Monte Carlo Methods in Practice, Springer.","DOI":"10.1007\/978-1-4757-3437-9_19"},{"key":"ref_50","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_51","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1177\/0278364915614638","article-title":"University of Michigan North Campus long-term vision and lidar dataset","volume":"35","author":"Ushani","year":"2016","journal-title":"Int. J. Robot. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Krajn\u00edk, T., Pedre, S., and P\u0159eu\u010dil, L. (2013, January 25\u201329). Monocular navigation for long-term autonomy. Proceedings of the 2013 16th International Conference on Advanced Robotics (ICAR), Montevideo, Uruguay.","DOI":"10.1109\/ICAR.2013.6766591"},{"key":"ref_56","unstructured":"Neubert, P., and Protzel, P. (September, January 31). Benchmarking superpixel descriptors. Proceedings of the European Signal Processing Conference (EUSIPCO), Nice, France."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). SuperPoint: Self-Supervised Interest Point Detection and Description. Proceedings of the CVPR Deep Learning for Visual SLAM Workshop, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Halodov\u00e1, L., Dvo\u0159\u00e1kov\u00e1, E., Majer, F., Ulrich, J., Vintr, T., Kusumam, K., and Krajn\u00edk, T. (2019, January 29\u201331). Adaptive Image Processing Methods for Outdoor Autonomous Vehicles. Proceedings of the Modelling and Simulation for Autonomous Systems (MESAS), Palermo, Italy.","DOI":"10.1007\/978-3-030-14984-0_34"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Krajn\u00edk, T., Crist\u00f3foris, P., Nitsche, M., Kusumam, K., and Duckett, T. (2015, January 2\u20134). Image features and seasons revisited. Proceedings of the 2015 European Conference on Mobile Robots (ECMR), Lincoln, UK.","DOI":"10.1109\/ECMR.2015.7324193"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Nitsche, M., Pire, T., Krajn\u00edk, T., Kulich, M., and Mejail, M. (2014). Monte carlo localization for teach-and-repeat feature-based navigation. Conference Towards Autonomous Robotic Systems, Springer.","DOI":"10.1007\/978-3-319-10401-0_2"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/2975\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:53:05Z","timestamp":1760136785000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/2975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,13]]},"references-count":60,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22082975"],"URL":"https:\/\/doi.org\/10.3390\/s22082975","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,13]]}}}