{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:39:09Z","timestamp":1783571949588,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministry of Education, Universities and Research","doi-asserted-by":"publisher","award":["2017S559BB"],"award-info":[{"award-number":["2017S559BB"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In precision agriculture, remote sensing is an essential phase in assessing crop status and variability when considering both the spatial and the temporal dimensions. To this aim, the use of unmanned aerial vehicles (UAVs) is growing in popularity, allowing for the autonomous performance of a variety of in-field tasks which are not limited to scouting or monitoring. To enable autonomous navigation, however, a crucial capability lies in accurately locating the vehicle within the surrounding environment. This task becomes challenging in agricultural scenarios where the crops and\/or the adopted trellis systems can negatively affect GPS signal reception and localisation reliability. A viable solution to this problem can be the exploitation of high-accuracy 3D maps, which provide important data regarding crop morphology, as an additional input of the UAVs\u2019 localisation system. However, the management of such big data may be difficult in real-time applications. In this paper, an innovative 3D sensor fusion approach is proposed, which combines the data provided by onboard proprioceptive (i.e., GPS and IMU) and exteroceptive (i.e., ultrasound) sensors with the information provided by a georeferenced 3D low-complexity map. In particular, the parallel-cuts ellipsoid method is used to merge the data from the distance sensors and the 3D map. Then, the improved estimation of the UAV location is fused with the data provided by the GPS and IMU sensors, using a Kalman-based filtering scheme. The simulation results prove the efficacy of the proposed navigation approach when applied to a quadrotor that autonomously navigates between vine rows.<\/jats:p>","DOI":"10.3390\/rs14061374","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T21:44:17Z","timestamp":1647207857000},"page":"1374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["3D Distance Filter for the Autonomous Navigation of UAVs in Agricultural Scenarios"],"prefix":"10.3390","volume":"14","author":[{"given":"Cesare","family":"Donati","sequence":"first","affiliation":[{"name":"Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council of Italy, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3421-7614","authenticated-orcid":false,"given":"Martina","family":"Mammarella","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council of Italy, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5466-0918","authenticated-orcid":false,"given":"Lorenzo","family":"Comba","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council of Italy, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"},{"name":"Department of Agricultural, Forest and Food Sciences (DiSAFA), Universit\u00e0 degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4256-095X","authenticated-orcid":false,"given":"Alessandro","family":"Biglia","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences (DiSAFA), Universit\u00e0 degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8342-425X","authenticated-orcid":false,"given":"Paolo","family":"Gay","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences (DiSAFA), Universit\u00e0 degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2258-8971","authenticated-orcid":false,"given":"Fabrizio","family":"Dabbene","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council of Italy, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Elkaim, G.H., Lie, F.A.P., and Gebre-Egziabher, D. (2015). Principles of guidance, navigation, and control of UAVs. Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-90-481-9707-1_56"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications\u2014A review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"39830","DOI":"10.1109\/ACCESS.2020.2975643","article-title":"A review on challenges of autonomous mobile robot and sensor fusion methods","volume":"8","author":"Alatise","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Schirrmann, M., Hamdorf, A., Giebel, A., Gleiniger, F., Pflanz, M., and Dammer, K.H. (2017). Regression kriging for improving crop height models fusing ultra-sonic sensing with UAV imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070665"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"White, F.E. (1991). Data Fusion Lexicon, Joint Directors of Laboratories (JDL). Technical Panel For C3.","DOI":"10.21236\/ADA529661"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"704504","DOI":"10.1155\/2013\/704504","article-title":"A review of data fusion techniques","volume":"2013","author":"Castanedo","year":"2013","journal-title":"Sci. World J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_8","first-page":"4465","article-title":"UAV attitude estimation using unscented Kalman filter and TRIAD","volume":"59","author":"Pereda","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mao, G., Drake, S., and Anderson, B.D. (2007, January 12\u201314). Design of an extended Kalman filter for UAV localization. Proceedings of the 2007 IEEE Information, Decision and Control, Adelaide, Australia.","DOI":"10.1109\/IDC.2007.374554"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Arellano-Cruz, L.A., Galvan-Tejada, G.M., and Lozano-Leal, R. (2020, January 11\u201313). Performance comparison of positioning algorithms for UAV navigation purposes based on estimated distances. Proceedings of the 2020 IEEE 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Mexico City, Mexico.","DOI":"10.1109\/CCE50788.2020.9299342"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2005.09.001","article-title":"Maximum posterior probability estimators of map accuracy","volume":"99","author":"Steele","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1109\/TAES.2011.6034661","article-title":"Novel technique for vision-based UAV navigation","volume":"47","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xie, W., Wang, L., Bai, B., Peng, B., and Feng, Z. (2019, January 20\u201324). An improved algorithm based on particle filter for 3D UAV target tracking. Proceedings of the IEEE International Conference on Communications (ICC 2019), Shanghai, China.","DOI":"10.1109\/ICC.2019.8762028"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Santos, N.P., Lobo, V., and Bernardino, A. (2019, January 16\u201319). Unmanned aerial vehicle tracking using a particle filter based approach. Proceedings of the 2019 IEEE Underwater Technology (UT), Kaohsiung, Taiwan.","DOI":"10.1109\/UT.2019.8734465"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rigatos, G. (2010, January 6\u20139). Distributed particle filtering over sensor networks for autonomous navigation of UAVs. Proceedings of the 2010 IEEE 72nd Vehicular Technology Conference, Ottawa, ON, Canada.","DOI":"10.1109\/VETECF.2010.5594384"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1007\/s12555-010-0608-7","article-title":"INS\/vSLAM system using distributed particle filter","volume":"8","author":"Won","year":"2010","journal-title":"Int. J. Control. Autom. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S1566-2535(03)00036-8","article-title":"Covariance consistency methods for fault-tolerant distributed data fusion","volume":"4","author":"Uhlmann","year":"2003","journal-title":"Inf. Fusion"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106301","DOI":"10.1016\/j.compag.2021.106301","article-title":"Using a depth camera for crop row detection and mapping for under-canopy navigation of agricultural robotic vehicle","volume":"188","author":"Gai","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.robot.2011.02.011","article-title":"Plant detection and mapping for agricultural robots using a 3D LIDAR sensor","volume":"59","author":"Weiss","year":"2011","journal-title":"Robot. Auton. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mammarella, M., Comba, L., Biglia, A., Dabbene, F., and Gay, P. (Biosyst. Eng., 2021). Cooperation of unmanned systems for agricultural applications: A theoretical framework, Biosyst. Eng., in press.","DOI":"10.1016\/j.biosystemseng.2021.11.008"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105121","DOI":"10.1016\/j.compag.2019.105121","article-title":"Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow","volume":"168","author":"Gregorio","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105579","DOI":"10.1016\/j.compag.2020.105579","article-title":"Real-time 3D unstructured environment reconstruction utilizing VR and Kinect-based immersive teleoperation for agricultural field robots","volume":"175","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Comba, L., Biglia, A., Ricauda Aimonino, D., Barge, P., Tortia, C., and Gay, P. (2019, January 24\u201326). 2D and 3D data fusion for crop monitoring in precision agriculture. Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy.","DOI":"10.1109\/MetroAgriFor.2019.8909219"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compag.2007.07.007","article-title":"Stereo vision three-dimensional terrain maps for precision agriculture","volume":"60","author":"Zhang","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105937","DOI":"10.1016\/j.compag.2020.105937","article-title":"Stereo-vision-based crop height estimation for agricultural robots","volume":"181","author":"Kim","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106445","DOI":"10.1016\/j.compag.2021.106445","article-title":"3D point cloud semantic segmentation toward large-scale unstructured agricultural scene classification","volume":"190","author":"Chen","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11119-019-09699-x","article-title":"Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery","volume":"21","author":"Comba","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.compenvurbsys.2008.06.003","article-title":"Comparison of region approximation techniques based on Delaunay triangulations and Voronoi diagrams","volume":"32","author":"Barclay","year":"2008","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.cageo.2012.06.004","article-title":"Implementation of a feature-constraint mesh generation algorithm within a GIS","volume":"49","author":"Heinzer","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.compag.2006.01.002","article-title":"Microscale modelling of fruit tissue using Voronoi tessellations","volume":"52","author":"Mebatsion","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.cad.2009.04.014","article-title":"Adaptive triangular-mesh reconstruction by mean-curvature-based refinement from point clouds using a moving parabolic approximation","volume":"42","author":"Yang","year":"2010","journal-title":"Comput.-Aided Des."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.2514\/3.11793","article-title":"Adaptive refinement-coarsening scheme for three-dimensional unstructured meshes","volume":"31","author":"Kallinderis","year":"1993","journal-title":"AIAA J."},{"key":"ref_33","first-page":"100025","article-title":"A geometric-based data reduction approach for large low dimensional datasets: Delaunay triangulation in SVM algorithms","volume":"4","author":"Almasi","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106237","DOI":"10.1016\/j.compag.2021.106237","article-title":"3D global mapping of large-scale unstructured orchard integrating eye-in-hand stereo vision and SLAM","volume":"187","author":"Chen","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.biosystemseng.2020.05.013","article-title":"Semantic interpretation and complexity reduction of 3D point clouds of vineyards","volume":"197","author":"Comba","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mammarella, M., Comba, L., Biglia, A., Dabbene, F., and Gay, P. (Biosyst. Eng., 2021). Cooperation of unmanned systems for agricultural applications: A case study in a vineyard, Biosyst. Eng., in press.","DOI":"10.1016\/j.biosystemseng.2021.12.010"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Donati, C., Mammarella, M., Comba, L., Biglia, A., Dabbene, F., and Gay, P. (2021, January 3\u20135). Improving agricultural drone localization using georeferenced low-complexity maps. Proceedings of the 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento-Bolzano, Italy.","DOI":"10.1109\/MetroAgriFor52389.2021.9628607"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Capello, E., Quagliotti, F., and Tempo, R. (2013, January 28\u201331). Randomized approaches and adaptive control for quadrotor UAVs. Proceedings of the IEEE 2013 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA.","DOI":"10.1109\/ICUAS.2013.6564721"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Powers, C., Mellinger, D., and Kumar, V. (2015). Quadrotor kinematics and dynamics. Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-90-481-9707-1_71"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/BF01299543","article-title":"The ellipsoid algorithm using parallel cuts","volume":"2","author":"Liao","year":"1993","journal-title":"Comput. Optim. Appl."},{"key":"ref_41","unstructured":"Kailath, T., Sayed, A.H., and Hassibi, B. (2000). Linear Estimation, Prentice Hall."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A compilation of UAV applications for precision agriculture","volume":"172","author":"Sarigiannidis","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_43","first-page":"507","article-title":"Agriculture drones: A modern breakthrough in precision agriculture","volume":"20","author":"Puri","year":"2017","journal-title":"J. Stat. Manag. Syst."},{"key":"ref_44","unstructured":"(2022, January 31). DJI Matrice 100 Technical Sheet. Available online: https:\/\/www.dji.com\/it\/matrice100\/info#specs."},{"key":"ref_45","unstructured":"(2022, January 31). VectorNav VN-200 GNSS\/IMU Datasheet. Available online: https:\/\/www.vectornav.com\/products\/detail\/vn-200."},{"key":"ref_46","unstructured":"(2022, January 31). Ultrasonic Ranging Module HC-SR04 Datasheet. Available online: https:\/\/www.elecrow.com\/hcsr04-ultrasonic-ranging-sensor-p-316.html."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Christiansen, M.P., Laursen, M.S., J\u00f8rgensen, R.N., 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_48","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.biosystemseng.2010.11.010","article-title":"Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)","volume":"108","author":"Xiang","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_49","first-page":"89","article-title":"Multi-sensor fusion based UAV collision avoidance system","volume":"76","author":"Rambabu","year":"2015","journal-title":"J. Teknol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Oh, K.H., and Ahn, H.S. (2015, January 13\u201316). Extended Kalman filter with multi-frequency reference data for quadrotor navigation. Proceedings of the 2015 15th International Conference on Control, Automation and Systems (ICCAS), Busan, Korea.","DOI":"10.1109\/ICCAS.2015.7364907"},{"key":"ref_51","unstructured":"Wang, Y., Nguyen, B.M., Kotchapansompote, P., Fujimoto, H., and Hori, Y. (2012, January 28\u201331). Vision-based vehicle body slip angle estimation with multi-rate Kalman filter considering time delay. Proceedings of the 2012 IEEE International Symposium on Industrial Electronics, Hangzhou, China."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:35:10Z","timestamp":1760135710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,11]]},"references-count":51,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061374"],"URL":"https:\/\/doi.org\/10.3390\/rs14061374","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,11]]}}}