{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:16:03Z","timestamp":1771024563007,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Andalusian project","award":["UMA18-FEDERJA-090"],"award-info":[{"award-number":["UMA18-FEDERJA-090"]}]},{"name":"Andalusian project","award":["RTI2018-093421-B-I00"],"award-info":[{"award-number":["RTI2018-093421-B-I00"]}]},{"name":"Spanish project","award":["UMA18-FEDERJA-090"],"award-info":[{"award-number":["UMA18-FEDERJA-090"]}]},{"name":"Spanish project","award":["RTI2018-093421-B-I00"],"award-info":[{"award-number":["RTI2018-093421-B-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a new synthetic dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released.<\/jats:p>","DOI":"10.3390\/s22155599","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T04:59:16Z","timestamp":1658897956000},"page":"5599","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Automatically Annotated Dataset of a Ground Mobile Robot in Natural Environments via Gazebo Simulations"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2303-1742","authenticated-orcid":false,"given":"Manuel","family":"S\u00e1nchez","sequence":"first","affiliation":[{"name":"Robotics and Mechatronics Lab, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1095-4775","authenticated-orcid":false,"given":"Jes\u00fas","family":"Morales","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Lab, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8940-2465","authenticated-orcid":false,"given":"Jorge L.","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Lab, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8174-1331","authenticated-orcid":false,"given":"J. J.","family":"Fern\u00e1ndez-Lozano","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Lab, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3432-3230","authenticated-orcid":false,"given":"Alfonso","family":"Garc\u00eda-Cerezo","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Lab, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guastella, D.C., and Muscato, G. (2021). Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review. Sensors, 21.","DOI":"10.3390\/s21010073"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5194\/isprs-annals-IV-1-W1-91-2017","article-title":"SEMANTIC3D.NET: A new large-scale point cloud classification benchmark","volume":"IV-1-W1","author":"Hackel","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364916679498","article-title":"1 year, 1000 km: The Oxford RobotCar dataset","volume":"36","author":"Maddern","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_5","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. (November, January 27). SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s10514-009-9138-7","article-title":"A collection of outdoor robotic datasets with centimeter-accuracy ground truth","volume":"27","author":"Blanco","year":"2009","journal-title":"Auton. Robot."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aybakan, A., Haddeler, G., Akay, M.C., Ervan, O., and Temeltas, H. (2019, January 22\u201324). A 3D LiDAR Dataset of ITU Heterogeneous Robot Team. Proceedings of the ACM 5th International Conference on Robotics and Artificial Intelligence, Singapore.","DOI":"10.1145\/3373724.3373734"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/LRA.2015.2509024","article-title":"A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots","volume":"1","author":"Giusti","year":"2016","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/LRA.2019.2894468","article-title":"AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming","volume":"4","author":"Potena","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1177\/0278364913478897","article-title":"The Canadian planetary emulation terrain 3D mapping dataset","volume":"32","author":"Tong","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364917737153","article-title":"The Katwijk beach planetary rover dataset","volume":"37","author":"Hewitt","year":"2018","journal-title":"Int. J. Robot. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1177\/02783649211004959","article-title":"The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises","volume":"40","author":"Morales","year":"2021","journal-title":"Int. J. Robot. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tan, W., Qin, N., Ma, L., Li, Y., Du, J., Cai, G., Yang, K., and Li, J. (2020, January 14\u201319). Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00109"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuScenes: A Multimodal Dataset for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chang, M.F., Lambert, J.W., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., and Ramanan, D. (2019, January 15\u201320). Argoverse: 3D Tracking and Forecasting with Rich Maps. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, M., Mart\u00ednez, J.L., Morales, J., Robles, A., and Mor\u00e1n, M. (2019, January 18\u201320). Automatic Generation of Labeled 3D Point Clouds of Natural Environments with Gazebo. Proceedings of the IEEE International Conference on Mechatronics (ICM), Ilmenau, Germany.","DOI":"10.1109\/ICMECH.2019.8722866"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, R., Candra, S.A., Vetter, K., and Zakhor, A. (2015, January 26\u201330). Sensor fusion for semantic segmentation of urban scenes. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139439"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"87695","DOI":"10.1109\/ACCESS.2020.2992612","article-title":"CSPC-Dataset: New LiDAR Point Cloud Dataset and Benchmark for Large-Scale Scene Semantic Segmentation","volume":"8","author":"Tong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, J.L., Mor\u00e1n, M., Morales, J., Robles, A., and S\u00e1nchez, M. (2020). Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans. Appl. Sci., 10.","DOI":"10.3390\/app10031140"},{"key":"ref_21","unstructured":"Griffiths, D., and Boehm, J. (2019). SynthCity: A large scale synthetic point cloud. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/978-3-030-75178-4_7","article-title":"Synthetic Simulated Environments","volume":"Volume 174","author":"Nikolenko","year":"2021","journal-title":"Synthetic Data for Deep Learning"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yue, X., Wu, B., Seshia, S.A., Keutzer, K., and Sangiovanni-Vincentelli, A.L. (2018, January 11\u201314). A LiDAR Point Cloud Generator: From a Virtual World to Autonomous Driving. Proceedings of the ACM International Conference on Multimedia Retrieval, Yokohama, Japan.","DOI":"10.1145\/3206025.3206080"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hurl, B., Czarnecki, K., and Waslander, S. (2019, January 9\u201312). Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813809"},{"key":"ref_25","unstructured":"Khan, S., Phan, B., Salay, R., and Czarnecki, K. (2019, January 15\u201320). ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1729881417735401","article-title":"Training a terrain traversability classifier for a planetary rover through simulation","volume":"14","author":"Hewitt","year":"2017","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_28","first-page":"177","article-title":"Unmanned Ground Vehicles in Precision Farming Services: An Integrated Emulation Modelling Approach","volume":"Volume 953","author":"Bechtsis","year":"2019","journal-title":"Information and Communication Technologies in Modern Agricultural Development"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TASE.2014.2368997","article-title":"Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response","volume":"12","author":"Koenig","year":"2015","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, J.L., Morales, J., S\u00e1nchez, M., Mor\u00e1n, M., Reina, A.J., and Fern\u00e1ndez-Lozano, J.J. (2020). Reactive Navigation on Natural Environments by Continuous Classification of Ground Traversability. Sensors, 20.","DOI":"10.3390\/s20226423"},{"key":"ref_31","unstructured":"Koenig, K., and Howard, A. (October, January 28). Design and use paradigms for Gazebo, an open-source multi-robot simulator. Proceedings of the IEEE-RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hosseininaveh, A., and Remondino, F. (2021). An Imaging Network Design for UGV-Based 3D Reconstruction of Buildings. Remote Sens., 13.","DOI":"10.3390\/rs13101923"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A. (2009, January 12\u201317). ROS: An open-source Robot Operating System. Proceedings of the IEEE ICRA Workshop on Open Source Software, Kobe, Japan.","DOI":"10.1109\/MRA.2010.936956"},{"key":"ref_34","first-page":"3523","article-title":"Image Segmentation Using Deep Learning: A Survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sickert, S., and Denzler, J. (2017, January 12\u201315). Semantic Segmentation of Outdoor Areas using 3D Moment Invariants and Contextual Cues. Proceedings of the German Conference on Pattern Recognition (GCPR), Basel, Switzerland.","DOI":"10.1007\/978-3-319-66709-6_14"},{"key":"ref_36","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 Trans. Robot."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dai, J., Li, D., Li, Y., Zhao, J., Li, W., and Liu, G. (2022). Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud. Sensors, 22.","DOI":"10.3390\/s22114114"},{"key":"ref_38","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., L\u00f3pez, A., and Koltun, V. (2017, January 13\u201315). CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Conference on Robot Learning, Mountain View, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Palafox, P.R., Garz\u00f3n, M., Valente, J., Rold\u00e1n, J.J., and Barrientos, A. (2019). Robust Visual-Aided Autonomous Takeoff, Tracking, and Landing of a Small UAV on a Moving Landing Platform for Life-Long Operation. Appl. Sci., 9.","DOI":"10.3390\/app9132661"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5599\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:57:02Z","timestamp":1760140622000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":39,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155599"],"URL":"https:\/\/doi.org\/10.3390\/s22155599","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,26]]}}}