{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:52:06Z","timestamp":1778781126067,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During the last decades, consumer-grade RGB-D (red green blue-depth) cameras have gained popularity for several applications in agricultural environments. Interestingly, these cameras are used for spatial mapping that can serve for robot localization and navigation. Mapping the environment for targeted robotic applications in agricultural fields is a particularly challenging task, owing to the high spatial and temporal variability, the possible unfavorable light conditions, and the unpredictable nature of these environments. The aim of the present study was to investigate the use of RGB-D cameras and unmanned ground vehicle (UGV) for autonomously mapping the environment of commercial orchards as well as providing information about the tree height and canopy volume. The results from the ground-based mapping system were compared with the three-dimensional (3D) orthomosaics acquired by an unmanned aerial vehicle (UAV). Overall, both sensing methods led to similar height measurements, while the tree volume was more accurately calculated by RGB-D cameras, as the 3D point cloud captured by the ground system was far more detailed. Finally, fusion of the two datasets provided the most precise representation of the trees.<\/jats:p>","DOI":"10.3390\/s22041571","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T20:26:41Z","timestamp":1645129601000},"page":"1571","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5743-625X","authenticated-orcid":false,"given":"Aristotelis C.","family":"Tagarakis","sequence":"first","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evangelia","family":"Filippou","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Damianos","family":"Kalaitzidis","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lefteris","family":"Benos","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrizia","family":"Busato","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7058-5986","authenticated-orcid":false,"given":"Dionysis","family":"Bochtis","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece"},{"name":"FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1109\/TCYB.2013.2265378","article-title":"Enhanced Computer Vision with Microsoft Kinect Sensor: A Review","volume":"43","author":"Han","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ganganath, N., and Leung, H. (2012, January 12\u201314). Mobile robot localization using odometry and kinect sensor. Proceedings of the 2012 IEEE International Conference on Emerging Signal Processing Applications, Las Vegas, NV, USA.","DOI":"10.1109\/ESPA.2012.6152453"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris, D., and Bochtis, D. (2021). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors, 21.","DOI":"10.3390\/s21113758"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lindner, L., Sergiyenko, O., Rivas-L\u00f3pez, M., Ivanov, M., Rodr\u00edguez-Qui\u00f1onez, J.C., Hern\u00e1ndez-Balbuena, D., Flores-Fuentes, W., Tyrsa, V., Muerrieta-Rico, F.N., and Mercorelli, P. (2017, January 19\u201321). Machine vision system errors for unmanned aerial vehicle navigation. Proceedings of the 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK.","DOI":"10.1109\/ISIE.2017.8001488"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2016.06.022","article-title":"A review of key techniques of vision-based control for harvesting robot","volume":"127","author":"Zhao","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.biosystemseng.2019.04.024","article-title":"A novel image processing algorithm to separate linearly clustered kiwifruits","volume":"183","author":"Fu","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105687","DOI":"10.1016\/j.compag.2020.105687","article-title":"Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review","volume":"177","author":"Fu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.3390\/s120201437","article-title":"Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications","volume":"12","author":"Khoshelham","year":"2012","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"19058","DOI":"10.1364\/OE.392414","article-title":"Approach for accurate calibration of RGB-D cameras using spheres","volume":"28","author":"Liu","year":"2020","journal-title":"Opt. Express"},{"key":"ref_10","first-page":"173","article-title":"Geometric investigation of a gaming active device","volume":"Volume 8085","author":"Remondino","year":"2011","journal-title":"Proceedings of the Videometrics, Range Imaging, and Applications XI"},{"key":"ref_11","unstructured":"Khatib, O., Kumar, V., and Sukhatme, G. (2014). RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments BT\u2014Experimental Robotics. Proceedings of the 12th International Symposium on Experimental Robotics, New Delhi and Agra, India, 18\u201321 December 2010, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TRO.2013.2279412","article-title":"3-D Mapping with an RGB-D camera","volume":"30","author":"Endres","year":"2014","journal-title":"IEEE Trans. Robot."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Butkiewicz, T. (2014, January 14\u201319). Low-cost coastal mapping using Kinect v2 time-of-flight cameras. Proceedings of the 2014 Oceans\u2014St. John\u2019s, St. John\u2019s, NL, Canada.","DOI":"10.1109\/OCEANS.2014.7003084"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.neucom.2015.10.104","article-title":"Dense 3D reconstruction combining depth and RGB information","volume":"175","author":"Pan","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Herbst, E., Henry, P., Ren, X., and Fox, D. (2011, January 9\u201313). Toward object discovery and modeling via 3-D scene comparison. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980542"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4893","DOI":"10.1109\/TIP.2014.2352851","article-title":"Robust 3D Reconstruction with an RGB-D Camera","volume":"23","author":"Wang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.biosystemseng.2020.09.009","article-title":"Safety and ergonomics in human-robot interactive agricultural operations","volume":"200","author":"Benos","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Benos, L., Tsaopoulos, D., and Bochtis, D. (2020). A Review on Ergonomics in Agriculture. Part II: Mechanized Operations. Appl. Sci., 10.","DOI":"10.3390\/app10103484"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.protcy.2012.03.021","article-title":"Detecting objects using color and depth segmentation with Kinect sensor","volume":"3","year":"2012","journal-title":"Procedia Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104619","DOI":"10.1016\/j.dib.2019.104619","article-title":"A multi-camera dataset for depth estimation in an indoor scenario","volume":"27","author":"Marin","year":"2019","journal-title":"Data Br."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.30534\/ijeter\/2020\/85852020","article-title":"A study on determination of simple objects volume using ZED stereo camera based on 3D-points and segmentation images","volume":"8","author":"Tran","year":"2020","journal-title":"Int. J. Emerg. Trends Eng. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.5194\/isprs-archives-XLII-2-W8-237-2017","article-title":"A cost-effective method for crack detection and measurement on concrete surface","volume":"42","author":"Sarker","year":"2017","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"00020","DOI":"10.1051\/e3sconf\/20186300020","article-title":"Low cost real time UAV stereo photogrammetry modelling technique-accuracy considerations","volume":"Volume 63","author":"Burdziakowski","year":"2018","journal-title":"Proceedings of the E3S Web of Conferences"},{"key":"ref_24","first-page":"1","article-title":"Indoor mapping for Smart Cities\u2014An affordable approach: Using kinect sensor and ZED stereo camera","volume":"Volume 2017","author":"Gupta","year":"2017","journal-title":"Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105394","DOI":"10.1016\/j.compag.2020.105394","article-title":"Evaluation of low-cost depth cameras for agricultural applications","volume":"173","author":"Condotta","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"And\u00fajar, D., Dorado, J., Fern\u00e1ndez-Quintanilla, C., and Ribeiro, A. (2016). An Approach to the Use of Depth Cameras for Weed Volume Estimation. Sensors, 16.","DOI":"10.3390\/s16070972"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.3390\/s130202384","article-title":"Rapid Characterization of Vegetation Structure with a Microsoft Kinect Sensor","volume":"13","author":"Azzari","year":"2013","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.compag.2016.01.018","article-title":"Using depth cameras to extract structural parameters to assess the growth state and yield of cauliflower crops","volume":"122","author":"Ribeiro","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1093\/aob\/mcy016","article-title":"Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations","volume":"121","author":"Hui","year":"2018","journal-title":"Ann. Bot."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.3389\/fpls.2017.02233","article-title":"Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera","volume":"8","author":"Jiang","year":"2018","journal-title":"Front. Plant. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vit, A., and Shani, G. (2018). Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping. Sensors, 18.","DOI":"10.20944\/preprints201810.0664.v1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Z., Walsh, K.B., and Verma, B. (2017). On-Tree Mango Fruit Size Estimation Using RGB-D Images. Sensors, 17.","DOI":"10.3390\/s17122738"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105523","DOI":"10.1016\/j.compag.2020.105523","article-title":"UAV environmental perception and autonomous obstacle avoidance: A deep learning and depth camera combined solution","volume":"175","author":"Wang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.compag.2018.11.026","article-title":"In-field high throughput grapevine phenotyping with a consumer-grade depth camera","volume":"156","author":"Milella","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/LRA.2017.2651952","article-title":"Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting\u2014Combined Color and 3-D Information","volume":"2","author":"Sa","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Konolige, K. (2010, January 3\u20137). Projected texture stereo. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509796"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10514-012-9321-0","article-title":"OctoMap: An efficient probabilistic 3D mapping framework based on octrees","volume":"34","author":"Hornung","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_38","unstructured":"(2021, December 13). ROS\u2014Robot Operating System. Available online: https:\/\/www.ros.org\/."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"De Silva, K.T.D.S., Cooray, B.P.A., Chinthaka, J.I., Kumara, P.P., and Sooriyaarachchi, S.J. (2019). Comparative Analysis of Octomap and RTABMap for Multi-Robot Disaster Site Mapping, Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/ICTER.2018.8615469"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Anagnostis, A., Tagarakis, A.C., Kateris, D., Moysiadis, V., S\u00f8rensen, C.G., Pearson, S., and Bochtis, D. (2021). Orchard Mapping with Deep Learning Semantic Segmentation. Sensors, 21.","DOI":"10.3390\/s21113813"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Christiansen, M., Laursen, M., J\u00f8rgensen, R., Skovsen, S., and Gislum, R. (2017). Designing and Testing a UAV Mapping System for Agricultural Field Surveying. Sensors, 17.","DOI":"10.3390\/s17122703"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"105385","DOI":"10.1016\/j.compag.2020.105385","article-title":"An automatic method for weed mapping in oat fields based on UAV imagery","volume":"173","author":"Zrinjski","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Veeranampalayam Sivakumar, A.N., Li, J., Scott, S., Psota, E., JJhala, A., Luck, J.D., and Shi, Y. (2020). Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12132136"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach","volume":"174","author":"Kerkech","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s11119-017-9558-x","article-title":"RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields","volume":"19","author":"Barrero","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Grimstad, L., and From, P.J. (2017). The Thorvald II Agricultural Robotic System. Robotics, 6.","DOI":"10.3390\/robotics6040024"},{"key":"ref_48","unstructured":"(2020, November 17). MeshLab. Available online: https:\/\/www.meshlab.net\/."},{"key":"ref_49","unstructured":"(2020, November 17). CloudCompare. Available online: http:\/\/www.cloudcompare.org\/."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Moysiadis, V., Tsolakis, N., Katikaridis, D., S\u00f8rensen, C.G., Pearson, S., and Bochtis, D. (2020). Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects. Appl. Sci., 10.","DOI":"10.3390\/app10103453"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.3390\/s140203001","article-title":"Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping","volume":"14","author":"Paulus","year":"2014","journal-title":"Sensors"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/978-3-319-26054-9_10","article-title":"Robotnik\u2014Professional service robotics applications with ROS","volume":"625","author":"Guzman","year":"2016","journal-title":"Stud. Comput. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Benos, L., Kokkotis, C., Tsatalas, T., Karampina, E., Tsaopoulos, D., and Bochtis, D. (2021). Biomechanical Effects on Lower Extremities in Human-Robot Collaborative Agricultural Tasks. Appl. Sci., 11.","DOI":"10.3390\/app112411742"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.opelre.2017.03.001","article-title":"Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods\u2019 approach","volume":"25","author":"Sergiyenko","year":"2017","journal-title":"Opto-Electron. Rev."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lindner, L., Sergiyenko, O., Rodr\u00edguez-Qui\u00f1onez, J.C., Tyrsa, V., Mercorelli, P., Fuentes, W.F., Murrieta-Rico, F.N., and Nieto-Hipolito, J.I. (2015, January 3\u20135). Continuous 3D scanning mode using servomotors instead of stepping motors in dynamic laser triangulation. Proceedings of the 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), Buzios, Brazil.","DOI":"10.1109\/ISIE.2015.7281598"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Rueda-Ayala, V.P., Pe\u00f1a, J.M., H\u00f6glind, M., Bengochea-Guevara, J.M., and And\u00fajar, D. (2019). Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley. Sensors, 19.","DOI":"10.3390\/s19030535"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1571\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:21:42Z","timestamp":1760134902000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,17]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041571"],"URL":"https:\/\/doi.org\/10.3390\/s22041571","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,17]]}}}