{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:00:41Z","timestamp":1783180841036,"version":"3.54.6"},"reference-count":102,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T00:00:00Z","timestamp":1608854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004505","name":"Universit\u00e0 di Catania","doi-asserted-by":"publisher","award":["Safe and Smart Farming with Artificial Intelligence and Robotics - programma ricerca di ateneo UNICT 2020\u201322 linea 2"],"award-info":[{"award-number":["Safe and Smart Farming with Artificial Intelligence and Robotics - programma ricerca di ateneo UNICT 2020\u201322 linea 2"]}],"id":[{"id":"10.13039\/501100004505","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.<\/jats:p>","DOI":"10.3390\/s21010073","type":"journal-article","created":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T09:30:19Z","timestamp":1608888619000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5007-7207","authenticated-orcid":false,"given":"Dario Calogero","family":"Guastella","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universit\u00e0 degli Studi di Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5027-9239","authenticated-orcid":false,"given":"Giovanni","family":"Muscato","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universit\u00e0 degli Studi di Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1017\/S0263574703004971","article-title":"The first biologically inspired robots","volume":"21","author":"Holland","year":"2003","journal-title":"Robotica"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wilcox, B., and Nguyen, T. (1998). Sojourner on Mars and Lessons Learned for Future Planetary Rovers. SAE Technical Paper, SAE International.","DOI":"10.4271\/981695"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Buehler, M., Iagnemma, K., and Singh, S. (2007). The 2005 DARPA Grand Challenge: The Great Robot Race, Springer.","DOI":"10.1007\/978-3-540-73429-1"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brock, O., Park, J., and Toussaint, M. (2016). Mobility and manipulation. Springer Handbook of Robotics, Springer.","DOI":"10.1007\/978-3-319-32552-1_40"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TIV.2016.2578706","article-title":"A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles","volume":"1","author":"Paden","year":"2016","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113816","DOI":"10.1016\/j.eswa.2020.113816","article-title":"Self-driving cars: A survey","volume":"165","author":"Badue","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_7","unstructured":"Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., and Zhang, J. (2016). End to End Learning for Self-Driving Cars. arXiv."},{"key":"ref_8","unstructured":"Liu, G.H., Siravuru, A., Prabhakar, S., Veloso, M., and Kantor, G. (2017). Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation. arXiv."},{"key":"ref_9","unstructured":"Gao, W., Hsu, D., Lee, W.S., Shen, S., and Subramanian, K. (2017). Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pfeiffer, M., Schaeuble, M., Nieto, J., Siegwart, R., and Cadena, C. (June, January 29). From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989182"},{"key":"ref_11","unstructured":"Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A.J., Banino, A., Denil, M., Goroshin, R., Sifre, L., and Kavukcuoglu, K. (2017). Learning to Navigate in Complex Environments. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/LRA.2020.2965857","article-title":"Deep Reinforcement Learning for Instruction Following Visual Navigation in 3D Maze-Like Environments","volume":"5","author":"Devo","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.jterra.2012.01.001","article-title":"Terrain trafficability analysis and soil mechanical property identification for planetary rovers: A survey","volume":"49","author":"Chhaniyara","year":"2012","journal-title":"J. Terramechanics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1016\/j.engappai.2013.01.006","article-title":"Terrain Traversability Analysis Methods for Unmanned Ground Vehicles: A Survey","volume":"26","author":"Papadakis","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/rob.21918","article-title":"A survey of deep learning techniques for autonomous driving","volume":"37","author":"Grigorescu","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kuutti, S., Bowden, R., Jin, Y., Barber, P., and Fallah, S. (2020). A Survey of Deep Learning Applications to Autonomous Vehicle Control. IEEE Trans. Intell. Transp. Syst., 1\u201322.","DOI":"10.1109\/TITS.2019.2962338"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., and Cao, W. (2020). A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods. Appl. Sci., 10.","DOI":"10.3390\/app10082749"},{"key":"ref_18","unstructured":"Tai, L., Zhang, J., Liu, M., Boedecker, J., and Burgard, W. (2018). A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation. arXiv."},{"key":"ref_19","unstructured":"Wulfmeier, M. (2018). On Machine Learning and Structure for Mobile Robots. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1631\/FITEE.1900518","article-title":"A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments","volume":"21","author":"Hu","year":"2020","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hussein, A., Gaber, M.M., Elyan, E., and Jayne, C. (2017). Imitation Learning: A Survey of Learning Methods. ACM Comput. Surv., 50.","DOI":"10.1145\/3054912"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ravichandar, H., Polydoros, A., Chernova, S., and Billard, A. (2020). Recent Advances in Robot Learning from Demonstration. Annu. Rev. Control. Robot. Auton. Syst., 3.","DOI":"10.1146\/annurev-control-100819-063206"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kober, J., and Peters, J. (2014). Reinforcement Learning in Robotics: A Survey. Learning Motor Skills: From Algorithms to Robot Experiments, Springer International Publishing.","DOI":"10.1007\/978-3-319-03194-1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1177\/0278364917722396","article-title":"Large-scale cost function learning for path planning using deep inverse reinforcement learning","volume":"36","author":"Wulfmeier","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1177\/0278364910392608","article-title":"Optimization and learning for rough terrain legged locomotion","volume":"30","author":"Zucker","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Herbrich, R., Graepel, T., and Obermayer, K. (1999, January 7\u201310). Support vector learning for ordinal regression. Proceedings of the 1999 Ninth International Conference on Artificial Neural Networks ICANN 99 (Conf. Publ. No. 470), Edinburgh, UK.","DOI":"10.1049\/cp:19991091"},{"key":"ref_27","unstructured":"Zucker, M. (2009). A Data-Driven Approach to High Level Planning, Carnegie Mellon University. Technical Report CMU-RI-TR-09-42."},{"key":"ref_28","first-page":"1153","article-title":"Boosting structured prediction for imitation learning","volume":"19","author":"Ratliff","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kolter, J.Z., Rodgers, M.P., and Ng, A.Y. (2008, January 19\u201323). A control architecture for quadruped locomotion over rough terrain. Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543305"},{"key":"ref_30","first-page":"769","article-title":"Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion","volume":"20","author":"Kolter","year":"2007","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1177\/0278364910387681","article-title":"An overview of the Defense Advanced Research Projects Agency\u2019s Learning Locomotion program","volume":"30","author":"Pippine","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1177\/0278364910369715","article-title":"Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain","volume":"29","author":"Silver","year":"2010","journal-title":"Int. J. Robot. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10514-009-9121-3","article-title":"Learning to search: Functional gradient techniques for imitation learning","volume":"27","author":"Ratliff","year":"2009","journal-title":"Auton. Robot."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Faigl, J., and Pr\u00e1gr, M. (2019). On Unsupervised Learning of Traversal Cost and Terrain Types Identification Using Self-organizing Maps. Artificial Neural Networks and Machine Learning\u2013ICANN 2019: Theoretical Neural Computation, Springer International Publishing.","DOI":"10.1007\/978-3-030-30487-4_50"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bekhti, M.A., and Kobayashi, Y. (2020). Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures. Appl. Sci., 10.","DOI":"10.3390\/app10041195"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/rob.21927","article-title":"Off-road ground robot path energy cost prediction through probabilistic spatial mapping","volume":"37","author":"Quann","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_37","first-page":"49","article-title":"Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization","volume":"Volume 48","author":"Balcan","year":"2016","journal-title":"Proceedings of Machine Learning Research"},{"key":"ref_38","unstructured":"Wulfmeier, M., Ondruska, P., and Posner, I. (2016). Maximum Entropy Deep Inverse Reinforcement Learning. arXiv."},{"key":"ref_39","first-page":"894","article-title":"Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories","volume":"87","author":"Zhang","year":"2018","journal-title":"Conf. Robot. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Li, N., Sun, R., Zhao, H., and Xu, D. (2019). Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning. arXiv.","DOI":"10.1109\/IV47402.2020.9304721"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1109\/LRA.2019.2895892","article-title":"Rover-IRL: Inverse Reinforcement Learning With Soft Value Iteration Networks for Planetary Rover Path Planning","volume":"4","author":"Pflueger","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1002\/rob.21521","article-title":"Terrain Classification in Complex Three-dimensional Outdoor Environments","volume":"32","author":"Teniente","year":"2015","journal-title":"J. Field Robot."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Suger, B., Steder, B., and Burgard, W. (2015, January 26\u201330). Traversability analysis for mobile robots in outdoor environments: A semi-supervised learning approach based on 3D-lidar data. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139749"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Elkan, C., and Noto, K. (2008, January 24\u201327). Learning classifiers from only positive and unlabeled data. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401920"},{"key":"ref_45","unstructured":"Denis, F., Gilleron, R., and Tommasi, M. (2002, January 1\u20135). Text classification from positive and unlabeled examples. Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Annecy, France."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s10514-016-9588-7","article-title":"An incremental nonparametric Bayesian clustering-based traversable region detection method","volume":"41","author":"Lee","year":"2017","journal-title":"Auton. Robots"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1002\/rob.21657","article-title":"Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments","volume":"34","author":"Ahtiainen","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sock, J., Kim, J., Min, J., and Kwak, K. (2016, January 16\u201321). Probabilistic traversability map generation using 3D-LIDAR and camera. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487782"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hewitt, R., Ellery, A., and Ruiter, A. (2017). Training a terrain traversability classifier for a planetary rover through simulation. Int. J. Adv. Robot. Syst., 14.","DOI":"10.1177\/1729881417735401"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Deng, F., Zhu, X., and He, C. (2017). Vision-Based Real-Time Traversable Region Detection for Mobile Robot in the Outdoors. Sensors, 17.","DOI":"10.3390\/s17092101"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/TITS.2017.2769218","article-title":"Learning Traversability From Point Clouds in Challenging Scenarios","volume":"19","author":"Bellone","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kingry, N., Jung, M., Derse, E., and Dai, R. (2018, January 1\u20135). Vision-Based Terrain Classification and Solar Irradiance Mapping for Solar-Powered Robotics. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593635"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, J., Moran, M., Morales, J., Robles, A., and Sanchez, M. (2020). Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans. Appl. Sci., 10.","DOI":"10.3390\/app10031140"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Schilling, F., Chen, X., Folkesson, J., and Jensfelt, P. (2017, January 24\u201328). Geometric and visual terrain classification for autonomous mobile navigation. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206092"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rothrock, B., Kennedy, R., Cunningham, C., Papon, J., Heverly, M., and Ono, M. (2016, January 13\u201316). SPOC: Deep Learning-based Terrain Classification for Mars Rover Missions. Proceedings of the AIAA SPACE 2016, Long Beach, CA, USA.","DOI":"10.2514\/6.2016-5539"},{"key":"ref_56","unstructured":"Valada, A., Oliveira, G., Brox, T., and Burgard, W. (2016, January 18\u201322). Towards robust semantic segmentation using deep fusion. Proceedings of the Workshop on Limits and Potentials of Deep Learning in Robotics at Robotics: Science and Systems (RSS), Ann Arbor, MI, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1109\/LRA.2018.2801794","article-title":"Learning Ground Traversability From Simulations","volume":"3","author":"Guzzi","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hutter, M., and Siegwart, R. (2018). Real-Time Semantic Mapping for Autonomous Off-Road Navigation. Field and Service Robotics, Springer International Publishing.","DOI":"10.1007\/978-3-319-67361-5"},{"key":"ref_59","unstructured":"Gonzalez, R., and Iagnemma, K. (2018). DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Holder, C.J., and Breckon, T.P. (2018, January 26\u201330). Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500526"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chiodini, S., Torresin, L., Pertile, M., and Debei, S. (2020). Evaluation of 3D CNN Semantic Mapping for Rover Navigation. arXiv.","DOI":"10.1109\/MetroAeroSpace48742.2020.9160157"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Roncancio, H., Becker, M., Broggi, A., and Cattani, S. (2014, January 8\u201311). Traversability analysis using terrain mapping and online-trained Terrain type classifier. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856427"},{"key":"ref_63","first-page":"829","article-title":"Bayesian Generalized Kernel Inference for Terrain Traversability Mapping","volume":"87","author":"Shan","year":"2018","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_64","first-page":"2546","article-title":"Nonparametric Bayesian inference on multivariate exponential families","volume":"Volume 27","author":"Ghahramani","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Suryamurthy, V., Raghavan, V.S., Laurenzi, A., Tsagarakis, N.G., and Kanoulas, D. (2019, January 15\u201317). Terrain Segmentation and Roughness Estimation using RGB Data: Path Planning Application on the CENTAURO Robot. Proceedings of the 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, ON, Canada.","DOI":"10.1109\/Humanoids43949.2019.9035009"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1109\/LRA.2019.2895390","article-title":"Where Should I Walk? Predicting Terrain Properties From Images Via Self-Supervised Learning","volume":"4","author":"Wellhausen","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_67","unstructured":"Zhou, R., Feng, W., Yang, H., Gao, H., Li, N., Deng, Z., and Ding, L. (2020). Predicting Terrain Mechanical Properties in Sight for Planetary Rovers with Semantic Clues. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Palazzo, S., Guastella, D.C., Cantelli, L., Spadaro, P., Rundo, F., Muscato, G., Giordano, D., and Spampinato, C. (2020, January 25\u201329). Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss. Proceedings of the 2020 IEEE International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341044"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Weiss, Y., Sch\u00f6lkopf, B., and Platt, J.C. (2006). Off-Road Obstacle Avoidance through End-to-End Learning. Advances in Neural Information Processing Systems 18, MIT Press.","DOI":"10.7551\/mitpress\/7503.001.0001"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ostafew, C.J., Schoellig, A.P., and Barfoot, T.D. (2013, January 3\u20137). Visual teach and repeat, repeat, repeat: Iterative Learning Control to improve mobile robot path tracking in challenging outdoor environments. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696350"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1002\/rob.4620010203","article-title":"Bettering operation of Robots by learning","volume":"1","author":"Arimoto","year":"1984","journal-title":"J. Robot. Syst."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zhang, K., Niroui, F., Ficocelli, M., and Nejat, G. (2018, January 6\u20138). Robot Navigation of Environments with Unknown Rough Terrain Using deep Reinforcement Learning. Proceedings of the 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Philadelphia, PA, USA.","DOI":"10.1109\/SSRR.2018.8468643"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ifacol.2019.12.505","article-title":"End-to-end Learning for Autonomous Crop Row-following","volume":"52","author":"Bakken","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1177\/0278364919880273","article-title":"Imitation learning for agile autonomous driving","volume":"39","author":"Pan","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Nguyen, N., Tran, K., Tjiputra, E., and Tran, Q.D. (2020, January 25\u201329). Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network. Proceedings of the 2020 IEEE International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341494"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Kahn, G., Abbeel, P., and Levine, S. (2020). BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv.","DOI":"10.1109\/LRA.2021.3057023"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Kahn, G., Villaflor, A., Ding, B., Abbeel, P., and Levine, S. (2018, January 21\u201325). Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460655"},{"key":"ref_78","first-page":"806","article-title":"Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation","volume":"Volume 87","author":"Billard","year":"2018","journal-title":"Proceedings of Machine Learning Research"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6748","DOI":"10.1109\/LRA.2020.3011912","article-title":"Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain","volume":"5","author":"Josef","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Manderson, T., Wapnick, S., Meger, D., and Dudek, G. (August, January 31). Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196879"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1008831426966","article-title":"Traversability analysis and path planning for a planetary rover","volume":"6","author":"Gennery","year":"1999","journal-title":"Auton. Robots"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., and Pollefeys, M. (2017). Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-W1-91-2017"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Engelcke, M., Rao, D., Wang, D.Z., Tong, C.H., and Posner, I. (June, January 29). Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989161"},{"key":"ref_84","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. arXiv."},{"key":"ref_85","unstructured":"Qi, C.R., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1177\/0278364920908922","article-title":"The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset","volume":"39","author":"Lamarre","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1177\/0278364919841437","article-title":"The Rosario dataset: Multisensor data for localization and mapping in agricultural environments","volume":"38","author":"Pire","year":"2019","journal-title":"Int. J. Robot. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"63485","DOI":"10.1109\/ACCESS.2019.2916480","article-title":"Three-Dimensional Vibration-Based Terrain Classification for Mobile Robots","volume":"7","author":"Bai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., and Hutter, M. (2020). Learning quadrupedal locomotion over challenging terrain. Sci. Robot., 5.","DOI":"10.1126\/scirobotics.abc5986"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1177\/0278364918770733","article-title":"The limits and potentials of deep learning for robotics","volume":"37","author":"Brock","year":"2018","journal-title":"Int. J. Robot. Res."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"9","DOI":"10.21014\/acta_imeko.v8i4.680","article-title":"Coverage path planning for a flock of aerial vehicles to support autonomous rovers through traversability analysis","volume":"8","author":"Guastella","year":"2019","journal-title":"ACTA IMEKO"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Renaudeau, B., Labbani-Igbida, O., and Mourioux, G. (2019). Air-ground cooperative topometric mapping of traversable ground. Auton. Robot., 44.","DOI":"10.1007\/s10514-019-09872-1"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Wermelinger, M., Fankhauser, P., Diethelm, R., Kr\u00fcsi, P., Siegwart, R., and Hutter, M. (2016, January 9\u201314). Navigation planning for legged robots in challenging terrain. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759199"},{"key":"ref_94","first-page":"4529","article-title":"Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems","volume":"Volume 30","author":"Guyon","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_95","unstructured":"Li, Y., Song, J., and Ermon, S. (2017). InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations. arXiv."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1038\/nn.4650","article-title":"The hippocampus as a predictive map","volume":"20","author":"Stachenfeld","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1038\/nature11150","article-title":"Multiscale gigapixel photography","volume":"486","author":"Brady","year":"2012","journal-title":"Nature"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Posch, C., Matolin, D., and Wohlgenannt, R. (2008, January 18\u201321). An asynchronous time-based image sensor. Proceedings of the 2008 IEEE International Symposium on Circuits and Systems, Seattle, WA, USA.","DOI":"10.1109\/ISCAS.2008.4541871"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","article-title":"A 128\u00d7 128 120 dB 15 \u03bcs Latency Asynchronous Temporal Contrast Vision Sensor","volume":"43","author":"Lichtsteiner","year":"2008","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_100","unstructured":"Jol, H.M. (2008). Ground Penetrating Radar Theory and Applications, Elsevier."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MAES.2006.284378","article-title":"Preventing Damage by Hidden Objects in Vegetation","volume":"21","author":"Boryssenko","year":"2006","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_102","unstructured":"Hafner, R., Hertweck, T., Kl\u00f6ppner, P., Bloesch, M., Neunert, M., Wulfmeier, M., Tunyasuvunakool, S., Heess, N., and Riedmiller, M. (2020). Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/73\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:46:06Z","timestamp":1760179566000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/73"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,25]]},"references-count":102,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21010073"],"URL":"https:\/\/doi.org\/10.3390\/s21010073","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,25]]}}}