{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:39Z","timestamp":1760144739449,"version":"build-2065373602"},"reference-count":169,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:00:00Z","timestamp":1715644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ERDF","award":["FOMON\u2013ITMS 313011V465"],"award-info":[{"award-number":["FOMON\u2013ITMS 313011V465"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>GNSS\/INS-based positioning must be revised for forest mapping, especially inside the forest. This study deals with the issue of the processability of GNSS\/INS-positioned MLS data collected in the forest environment. GNSS time-based point clustering processed the misaligned MLS point clouds collected from skid trails under a forest canopy. The points of a point cloud with two misaligned copies of the forest scene were manually clustered iteratively until two partial point clouds with the single forest scene were generated using a histogram of GNSS time. The histogram\u2019s optimal bin width was the maximum bin width used to create the two correct point clouds. The influence of GNSS outage durations, signal strength statistics, and point cloud parameters on the optimal bin width were then analyzed using correlation and regression analyses. The results showed no significant influence of GNSS outage duration or GNSS signal strength from the time range of scanning the two copies of the forest scene on the optimal width. The optimal bin width was strongly related to the point distribution in time, especially by the duration of the scanned plot\u2019s occlusion from reviewing when the maximum occlusion period influenced the optimal bin width the most (R2 = 0.913). Thus, occlusion of the sub-plot scanning of tree trunks and the terrain outside it improved the processability of the MLS data. Therefore, higher stem density of a forest stand is an advantage in mapping as it increases the duration of the occlusions for a point cloud after it is spatially tiled.<\/jats:p>","DOI":"10.3390\/rs16101734","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T06:28:12Z","timestamp":1715668092000},"page":"1734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mobile Laser Scanning Data Collected under a Forest Canopy with GNSS\/INS-Positioned Systems: Possibilities of Processability Improvements"],"prefix":"10.3390","volume":"16","author":[{"given":"Juraj","family":"\u010ce\u0148ava","sequence":"first","affiliation":[{"name":"Department of Forest Resource Planning and Informatics, Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, 960 01 Zvolen, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4538-6894","authenticated-orcid":false,"given":"J\u00e1n","family":"Tu\u010dek","sequence":"additional","affiliation":[{"name":"Department of Forest Resource Planning and Informatics, Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, 960 01 Zvolen, Slovakia"}]},{"given":"Juli\u00e1na","family":"Chud\u00e1","sequence":"additional","affiliation":[{"name":"Department of Forest Harvesting, Logistics and Ameliorations, Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, 960 01 Zvolen, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2956-944X","authenticated-orcid":false,"given":"Milan","family":"Kore\u0148","sequence":"additional","affiliation":[{"name":"Department of Forest Resource Planning and Informatics, Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, 960 01 Zvolen, Slovakia"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hyypp\u00e4, E., Yu, X., Kaartinen, H., Hakala, T., Kukko, A., Vastaranta, M., and Hyypp\u00e4, J. (2020). Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests. Remote Sens., 12.","DOI":"10.3390\/rs12203327"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, H., Feng, Z., Shen, C., and Chen, P. (2019). Applicability of personal laser scanning in forestry inventory. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211392"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112102","DOI":"10.1016\/j.rse.2020.112102","article-title":"Terrestrial laser scanning in forest ecology: Expanding the horizon","volume":"251","author":"Calders","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"1","article-title":"Individual tree detection and area-based approach in retrieval of forest inventory characteristics from low-pulse airborne laser scanning data","volume":"22","author":"Vastaranta","year":"2011","journal-title":"Photogramm. J. Finl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sa\u010dkov, I., Kulla, L., and Bucha, T. (2019). A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests. Remote Sens., 11.","DOI":"10.3390\/rs11121431"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Goodbody, T.R., Tompalski, P., Coops, N.C., Hopkinson, C., Treitz, P., and van Ewijk, K. (2020). Forest Inventory and Diversity Attribute Modelling Using Structural and Intensity Metrics from Multi-Spectral Airborne Laser Scanning Data. Remote Sens., 12.","DOI":"10.3390\/rs12132109"},{"key":"ref_7","first-page":"630","article-title":"Modelling growing stock volume of forest stands with various ALS area-based approaches","volume":"94","author":"Parkitna","year":"2021","journal-title":"For. Int. J. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101754","DOI":"10.1016\/j.ecoinf.2022.101754","article-title":"Influences of vegetation, model, and data parameters on forest aboveground biomass assessment using an area-based approach","volume":"70","author":"Brovkina","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.06.021","article-title":"International benchmarking of terrestrial laser scanning approaches for forest inventories","volume":"144","author":"Liang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2020.03.021","article-title":"Under-canopy UAV laser scanning for accurate forest field measurements","volume":"164","author":"Hakala","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","unstructured":"Bruggisser, M.M. (2021). Improving Forest Mensurations with High Resolution Point Clouds. [Doctoral Dissertation, Technische Universit\u00e4t Wien]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100088","DOI":"10.1016\/j.fecs.2023.100088","article-title":"A tree detection method based on trunk point cloud section in dense plantation forest using drone LiDAR data","volume":"10","author":"Zhang","year":"2023","journal-title":"For. Ecosyst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gollob, C., Ritter, T., and Nothdurft, A. (2020). Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology. Remote Sens., 12.","DOI":"10.3390\/rs12091509"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aguiar, A.S., dos Santos, F.N., Cunha, J.B., Sobreira, H., and Sousa, A.J. (2020). Localization and Mapping for Robots in Agriculture and Forestry: A Survey. Robotics, 9.","DOI":"10.3390\/robotics9040097"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2387","DOI":"10.1080\/19475705.2021.1964617","article-title":"Mobile 3D scan LiDAR: A literature review","volume":"12","author":"Chiappini","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MGRS.2022.3168135","article-title":"Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions","volume":"10","author":"Liang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.37045\/aslh-2009-0009","article-title":"Algorithms for Stem Mapping by Means of Terrestrial Laser Scanning","volume":"5","author":"Brolly","year":"2009","journal-title":"Acta Silv. Et Lignaria Hung."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.3390\/rs6054323","article-title":"Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm","volume":"6","author":"Olofsson","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","first-page":"122","article-title":"Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods","volume":"63","author":"Bucha","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2016.11.012","article-title":"Feasibility of Terrestrial laser scanning for collecting stem volume information from single trees","volume":"123","author":"Saarinen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"41","DOI":"10.5194\/isprs-annals-III-1-41-2016","article-title":"Quality analysis and correction of mobile backpack laser scanning data","volume":"III-1","author":"Liang","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","unstructured":"Tjernqvist, M. (2017). Backpack-Based Inertial Navigation and LiDAR Mapping in Forest Environments, Ume\u00e5 University."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2020.01.018","article-title":"Accurate derivation of stem curve and volume using backpack mobile laser scanning","volume":"161","author":"Kukko","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"197","article-title":"Slam and ins based positional accuracy assessment of natural and artificial objects under the forest canopy","volume":"XLIII-B1-2","author":"Mikita","year":"2022","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.3390\/rs70101095","article-title":"Assessing Handheld Mobile Laser Scanners for Forest Surveys","volume":"7","author":"Ryding","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, S., Kang, F., Li, W., Kan, J., Zheng, Y., and He, G. (2019). Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment. Sensors, 19.","DOI":"10.3390\/s19143212"},{"key":"ref_27","first-page":"103025","article-title":"Automatic tree crown segmentation using dense forest point clouds from Personal Laser Scanning (PLS)","volume":"114","author":"Tockner","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compag.2017.07.015","article-title":"Tango in forests\u2014An initial experience of the use of the new Google technology in connection with forest inventory tasks","volume":"141","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1080\/2150704X.2020.1802528","article-title":"An early exploration of the use of the Microsoft Azure Kinect for estimation of urban tree Diameter at Breast Height","volume":"11","author":"McGlade","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gollob, C., Ritter, T., Kra\u00dfnitzer, R., Tockner, A., and Nothdurft, A. (2021). Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology. Remote Sens., 13.","DOI":"10.3390\/rs13163129"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127815","DOI":"10.1016\/j.ufug.2022.127815","article-title":"Best practices to use the iPad Pro LiDAR for some procedures of data acquisition in the urban forest","volume":"79","author":"Bobrowski","year":"2023","journal-title":"Urban For. Urban Green."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1080\/22797254.2018.1482733","article-title":"Integrating terrestrial and airborne laser scanning for the assessment of single-tree attributes in Mediterranean forest stands","volume":"51","author":"Giannetti","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14214\/sf.10075","article-title":"Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning","volume":"53","author":"Maltamo","year":"2019","journal-title":"Silva Fenn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s40725-022-00160-3","article-title":"Use of Individual Tree and Product Level Data to Improve Operational Forestry","volume":"8","author":"Keefe","year":"2022","journal-title":"Curr. For. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"213","DOI":"10.5552\/crojfe.2021.768","article-title":"Developing an Automated Monitoring System for Cable Yarding Systems","volume":"42","author":"Gallo","year":"2021","journal-title":"Croat. J. For. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"401","DOI":"10.5552\/crojfe.2023.2252","article-title":"Measurement of Individual Tree Parameters with Carriage-Based Laser Scanning in Cable Yarding Operations","volume":"44","author":"Gollob","year":"2023","journal-title":"Croat. J. For. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1111\/2041-210X.13906","article-title":"Estimating forest above-ground biomass with terrestrial laser scanning: Current status and future directions","volume":"13","author":"Demol","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Borsah, A.A., Nazeer, M., and Wong, M.S. (2023). LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations. Forests, 14.","DOI":"10.3390\/f14102095"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Loudermilk, E.L., Pokswinski, S., Hawley, C.M., Maxwell, A., Gallagher, M.R., Skowronski, N.S., Hudak, A.T., Hoffman, C., and Hiers, J.K. (2023). Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem. Fire, 6.","DOI":"10.1101\/2023.01.15.524107"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Fang, H. (2020). Estimation of LAI with the LiDAR Technology: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12203457"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"559","DOI":"10.11118\/actaun202068030559","article-title":"Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests","volume":"68","author":"Haninec","year":"2020","journal-title":"Acta Univ. Agric. Et Silvic. Mendel. Brun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.tree.2020.03.006","article-title":"Standardizing Ecosystem Morphological Traits from 3D Information Sources","volume":"35","author":"Valbuena","year":"2020","journal-title":"Trends Ecol. Evol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107752","DOI":"10.1016\/j.ecolind.2021.107752","article-title":"Quantifying 3D vegetation structure in wetlands using differently measured airborne laser scanning data","volume":"127","author":"Koma","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"314","DOI":"10.14214\/df.314","article-title":"Characterizing tree communities in space and time using point clouds","volume":"2021","author":"Yrttimaa","year":"2021","journal-title":"Diss. For."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Neuville, R., Bates, J.S., and Jonard, F. (2021). Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13030352"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1111\/ddi.13760","article-title":"Which metrics derived from airborne laser scanning are essential to measure the vertical profile of ecosystems?","volume":"29","author":"Kissling","year":"2023","journal-title":"Divers. Distrib."},{"key":"ref_47","first-page":"39","article-title":"Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward","volume":"29","author":"Cord","year":"2022","journal-title":"Divers. Distrib."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Qian, C., Liu, H., Tang, J., Chen, Y., Kaartinen, H., Kukko, A., Zhu, L., Liang, X., Chen, L., and Hyypp\u00e4, J. (2016). An Integrated GNSS\/INS\/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping. Remote Sens., 9.","DOI":"10.3390\/rs9010003"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2018.04.019","article-title":"In-situ measurements from mobile platforms: An emerging approach to address the old challenges associated with forest inventories","volume":"143","author":"Liang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1093\/aob\/mcab087","article-title":"Automatic extraction and measurement of individual trees from mobile laser scanning point clouds of forests","volume":"128","author":"Bienert","year":"2021","journal-title":"Ann. Bot."},{"key":"ref_51","first-page":"448","article-title":"Assessing the potential of mobile laser scanning for stand-level forest inventories in near-natural forests","volume":"96","author":"Seki","year":"2023","journal-title":"For. Int. J. For. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1093\/aob\/mcab111","article-title":"Terrestrial laser scanning: A new standard of forest measuring and modelling?","volume":"128","author":"Kaitaniemi","year":"2021","journal-title":"Ann. Bot."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hun\u010daga, M., Chud\u00e1, J., Toma\u0161t\u00edk, J., Sl\u00e1mov\u00e1, M., Kore\u0148, M., and Chud\u00fd, F. (2020). The Comparison of Stem Curve Accuracy Determined from Point Clouds Acquired by Different Terrestrial Remote Sensing Methods. Remote Sens., 12.","DOI":"10.3390\/rs12172739"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2018.11.012","article-title":"Efficient and robust lane marking extraction from mobile lidar point clouds","volume":"147","author":"Jung","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Balado, J., Gonz\u00e1lez, E., Arias, P., and Castro, D. (2020). Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12030442"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"103703","DOI":"10.1016\/j.autcon.2021.103703","article-title":"Scan-to-BIM for the infrastructure domain: Generation of IFC-compliant models of road infrastructure assets and semantics using 3D point cloud data","volume":"127","author":"Justo","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2857","DOI":"10.1109\/JSTARS.2021.3060568","article-title":"Toward Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1016\/j.measurement.2013.03.006","article-title":"Review of mobile mapping and surveying technologies","volume":"46","author":"Puente","year":"2013","journal-title":"Measurement"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"935","DOI":"10.2193\/0091-7648(2005)33[935:EOFCOG]2.0.CO;2","article-title":"Effect of forest canopy on GPS-based movement data","volume":"33","author":"DeCesare","year":"2005","journal-title":"Wildl. Soc. Bull."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3218","DOI":"10.3390\/f6093218","article-title":"Accuracy of Kinematic Positioning Using Global Satellite Navigation Systems under Forest Canopies","volume":"6","author":"Kaartinen","year":"2015","journal-title":"Forests"},{"key":"ref_61","first-page":"3","article-title":"Impacts of forest spatial structure on variation of the multipath phenomenon of navigation satellite signals","volume":"61","author":"Brach","year":"2019","journal-title":"Folia For. Pol. Ser. A For."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Feng, T., Chen, S., Feng, Z., Shen, C., and Tian, Y. (2021). Effects of Canopy and Multi-Epoch Observations on Single-Point Positioning Errors of a GNSS in Coniferous and Broadleaved Forests. Remote Sens., 13.","DOI":"10.3390\/rs13122325"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Rybansky, M., Kratochv\u00edl, V., Dohnal, F., Gerold, R., Kristalova, D., Stodola, P., and Nohel, J. (2023). GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation. Appl. Sci., 13.","DOI":"10.3390\/app13106142"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Merry, K., and Bettinger, P. (2019). Smartphone GPS accuracy study in an urban environment. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0219890"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Boguspayev, N., Akhmedov, D., Raskaliyev, A., Kim, A., and Sukhenko, A. (2023). A Comprehensive Review of GNSS\/INS Integration Techniques for Land and Air Vehicle Applications. Appl. Sci., 13.","DOI":"10.3390\/app13084819"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/JSEN.2023.3324019","article-title":"A Novel Multidimensional Hybrid Position Compensation Method for INS\/GPS Integrated Navigation Systems During GPS Outages","volume":"24","author":"Zhang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_67","first-page":"109","article-title":"Multipath Mitigation under Forest Canopies: A Choke Ring Antenna Solution","volume":"55","author":"Danskin","year":"2009","journal-title":"For. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"6573230","DOI":"10.1155\/2021\/6573230","article-title":"Real-Time Multipath Mitigation in Multi-GNSS Short Baseline Positioning via CNN-LSTM Method","volume":"2021","author":"Tao","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Brach, M. (2022). Rapid Static Positioning Using a Four System GNSS Receivers in the Forest Environment. Forests, 13.","DOI":"10.3390\/f13010045"},{"key":"ref_70","first-page":"1","article-title":"P3-LINS: Tightly Coupled PPP-GNSS\/INS\/LiDAR Navigation System With Effective Initialization","volume":"72","author":"Li","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.isprsjprs.2022.03.004","article-title":"Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads","volume":"187","author":"Pires","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105010","DOI":"10.1016\/j.compag.2019.105010","article-title":"Detection of forest road damage using mobile laser profilometry","volume":"166","author":"Allman","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Kweon, H., Seo, J.I., and Lee, J.-W. (2020). Assessing the Applicability of Mobile Laser Scanning for Mapping Forest Roads in the Republic of Korea. Remote Sens., 12.","DOI":"10.3390\/rs12091502"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MRA.2006.1678144","article-title":"Simultaneous localization and mapping: Part I","volume":"13","author":"Bailey","year":"2006","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Lu, Z., Hu, Z., and Uchimura, K. (2009, January 16\u201318). SLAM Estimation in Dynamic Outdoor Environments: A Review. Proceedings of the Intelligent Robotics and Applications: Second International Conference, ICIRA 2009, Singapore.","DOI":"10.1007\/978-3-642-10817-4_25"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Tourani, A., Bavle, H., Sanchez-Lopez, J.L., and Voos, H. (2022). Visual SLAM: What Are the Current Trends and What to Expect?. Sensors, 22.","DOI":"10.3390\/s22239297"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1080\/01431161.2017.1410248","article-title":"Automatic non-rigid registration of multi-strip point clouds from mobile laser scanning systems","volume":"39","author":"Yan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Chen, W., Zhou, C., Shang, G., Wang, X., Li, Z., Xu, C., and Hu, K. (2022). SLAM Overview: From Single Sensor to Heterogeneous Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14236033"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Nevalainen, P., Li, Q., Melkas, T., Riekki, K., Westerlund, T., and Heikkonen, J. (2020). Navigation and Mapping in Forest Environment Using Sparse Point Clouds. Remote Sens., 12.","DOI":"10.3390\/rs12244088"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1002\/rob.22077","article-title":"Recent developments and applications of simultaneous localization and mapping in agriculture","volume":"39","author":"Ding","year":"2022","journal-title":"J. Field Robot."},{"key":"ref_81","first-page":"1823","article-title":"Forest feature lidar slam (f2-lslam) and integrated scan simultaneous trajectory enhancement and mapping (is2-team) for accurate forest inventory using backpack systems","volume":"XLVIII-1\/W","author":"Zhao","year":"2023","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4588","DOI":"10.3390\/f6124390","article-title":"SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR","volume":"6","author":"Tang","year":"2015","journal-title":"Forests"},{"key":"ref_83","first-page":"165","article-title":"Hand-Held Personal Laser Scanning","volume":"42","author":"Liang","year":"2020","journal-title":"Croat. J. For. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Del Perugia, B., Giannetti, F., Chirici, G., and Travaglini, D. (2019). Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning. Forests, 10.","DOI":"10.3390\/f10030277"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-021-04555-y","article-title":"Mapping in unstructured natural environment: A sensor fusion framework for wearable sensor suites","volume":"3","author":"Chahine","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3390\/geomatics2020011","article-title":"A Practical Algorithm for the Viewpoint Planning of Terrestrial Laser Scanners","volume":"2","author":"Jia","year":"2022","journal-title":"Geomatics"},{"key":"ref_87","first-page":"103104","article-title":"Inventory of close-to-nature forest stands using terrestrial mobile laser scanning","volume":"115","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"\u010cer\u0148ava, J., Mokro\u0161, M., Tu\u010dek, J., Antal, M., and Slatkovsk\u00e1, Z. (2019). Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data. Remote Sens., 11.","DOI":"10.3390\/rs11060615"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Angelats, E., and Colomina, I. (2014). ONE STEP MOBILE MAPPING LASER AND CAMERA DATA ORIENTATION AND CALIBRATION. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XL-3\/W1.","DOI":"10.5194\/isprsarchives-XL-3-W1-15-2014"},{"key":"ref_90","first-page":"347","article-title":"A marker-free calibration method for mobile laser scanning point clouds correction","volume":"XLIII-B2-2","author":"Yang","year":"2020","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1002\/rob.21768","article-title":"Learning to detect misaligned point clouds","volume":"35","author":"Almqvist","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/2442071","article-title":"A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges","volume":"2021","author":"Li","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/LGRS.2013.2252417","article-title":"Time-Variant Registration of Point Clouds Acquired by a Mobile Mapping System","volume":"11","author":"Han","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"277","DOI":"10.5194\/isprsannals-II-5-W2-277-2013","article-title":"Accurate registration of MMS point clouds of urban areas using trajectory","volume":"II-5\/W2","author":"Takai","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_95","unstructured":"Huang, X., Mei, G., Zhang, J., and Abbas, R. (2021). A comprehensive survey on point cloud registration. arXiv."},{"key":"ref_96","unstructured":"Lyu, Y., Huang, X., and Zhang, Z. (2021). CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"104544","DOI":"10.1016\/j.infrared.2023.104544","article-title":"Fast clustering method of LiDAR point clouds from coarse-to-fine","volume":"129","author":"Guo","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0262-8856(92)90066-C","article-title":"Object modelling by registration of multiple range images","volume":"10","author":"Chen","year":"1992","journal-title":"Image Vis. Comput."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_100","unstructured":"Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient variants of the ICP algorithm. Proceedings of the Proceedings Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1127\/pfg\/2015\/0270","article-title":"A Correspondence Framework for ALS Strip Adjustments based on Variants of the ICP Algorithm","volume":"2015","author":"Glira","year":"2015","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Strand, M., Dillmann, R., Menegatti, E., and Ghidoni, S. (2019). Intelligent Autonomous Systems 15, Springer. IAS 2018, Advances in Intelligent Systems and Computing.","DOI":"10.1007\/978-3-030-01370-7"},{"key":"ref_103","unstructured":"CLOUD COMPARE (2023, April 14). CloudCompare Version 2.6.1\u2014User Manual. Available online: https:\/\/www.danielgm.net\/cc\/doc\/qCC\/CloudCompare%20v2.6.1%20-%20User%20manual.pdf."},{"key":"ref_104","unstructured":"Yang, X.-S. (2023, April 14). Cuckoo Search (cs) Algorithm. mATLAB Central File Exchange. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/29809-cuckoo-search-cs-algorithm."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"104757","DOI":"10.1016\/j.autcon.2023.104757","article-title":"Label-efficient semantic segmentation of large-scale industrial point clouds using weakly supervised learning","volume":"148","author":"Yin","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12205-023-2406-9","article-title":"Optimal Pre-processing of Laser Scanning Data for Indoor Scene Analysis and 3D Reconstruction of Building Models","volume":"28","author":"Kim","year":"2024","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_107","first-page":"180266","article-title":"A preprocessing method of 3D point clouds registration in urban environments","volume":"45","author":"Zhao","year":"2018","journal-title":"Opto-Electron. Eng."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Wang, P., Gu, T., Sun, B., Huang, D., and Sun, K. (2022). Research on 3D Point Cloud Data Preprocessing and Clustering Algorithm of Obstacles for Intelligent Vehicle. World Electr. Veh. J., 13.","DOI":"10.3390\/wevj13070130"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Wu, C., Yuan, Y., Tang, Y., and Tian, B. (2021). Application of Terrestrial Laser Scanning (TLS) in the Architecture, Engineering and Construction (AEC) Industry. Sensors, 22.","DOI":"10.3390\/s22010265"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/14498596.2007.9635123","article-title":"Pre-processing procedures for raw point clouds from terrestrial laser scanners","volume":"52","author":"Bae","year":"2007","journal-title":"J. Spat. Sci."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"012038","DOI":"10.1088\/1742-6596\/2185\/1\/012038","article-title":"Research on Fast Pre-Processing Method of Tunnel Point Cloud Data in Complex Environment","volume":"2185","author":"Zhu","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_112","unstructured":"Boavida, J., and Oliveira, A. (March, January 27). Precise Long Tunnel Survey using the Riegl VMX-250 Mobile Laser Scanning System. Proceedings of the 2012 RIEGL International Airborne and Mobile User Conference, Orlando, FL, USA."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Bienert, A., Georgi, L., Kunz, M., Maas, H.-G., and Von Oheimb, G. (2018). Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories. Forests, 9.","DOI":"10.3390\/f9070395"},{"key":"ref_114","unstructured":"Levene, H. (1960). Contributions to Probability and Statistics, Stanford Studies in Mathematics and Statistics, 2, Stanford University Press."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1214\/09-STS301","article-title":"The Impact of Levene\u2019s Test of Equality of Variances on Statistical Theory and Practice","volume":"24","author":"Gastwirth","year":"2009","journal-title":"Stat. Sci."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1177\/00045632211050531","article-title":"Best practice in statistics: The use of log transformation","volume":"59","author":"West","year":"2021","journal-title":"Ann. Clin. Biochem. Int. J. Biochem. Lab. Med."},{"key":"ref_117","first-page":"105","article-title":"Log-transformation and its implications for data analysis","volume":"26","author":"Feng","year":"2014","journal-title":"Shanghai Arch. Psychiatry"},{"key":"ref_118","first-page":"52","article-title":"Dilution of precision","volume":"10","author":"Langley","year":"1999","journal-title":"GPS World"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Kurum, M., Farhad, M.M., and Boyd, D.R. (2022, January 17\u201322). Gnss transmissometry (GNSS-T): Modeling propagation of GNSS signals through forest canopy. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883361"},{"key":"ref_120","first-page":"145","article-title":"Real-time lidar-inertial positioning and mapping for forestry automation","volume":"XLVIII-1\/W","author":"Faitli","year":"2023","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"100121","DOI":"10.1016\/j.srs.2024.100121","article-title":"Comparing positioning accuracy of mobile laser scanning systems under a forest canopy","volume":"9","author":"Muhojoki","year":"2024","journal-title":"Sci. Remote Sens."},{"key":"ref_122","first-page":"232","article-title":"A GNSS\/INS Integrated Navigation Algorithm Based on Kalman Filter","volume":"51","author":"Wang","year":"2018","journal-title":"IFAC-Pap."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Dong, Y., Wang, D., Zhang, L., Li, Q., and Wu, J. (2020). Tightly Coupled GNSS\/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation. Sensors, 20.","DOI":"10.3390\/s20020561"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Wu, J., Jiang, J., Zhang, C., Li, Y., Yan, P., and Meng, X. (2023). A Novel Optimal Robust Adaptive Scheme for Accurate GNSS RTK\/INS Tightly Coupled Integration in Urban Environments. Remote Sens., 15.","DOI":"10.3390\/rs15153725"},{"key":"ref_125","unstructured":"Boer, J.D., Calmettes, V., Tourneret, J., and Lesot, B. (2009, January 24\u201328). Outage mitigation for GNSS\/MEMS navigation using neural networks. Proceedings of the 2009 17th European Signal Processing Conference, Glasgow, UK."},{"key":"ref_126","unstructured":"Torino, P.D., Wu, F., and Dovis, F. (2020). SINS\/GNSS Tighty Coupled Integration based on a Radial Basis Function Neural Network. [Ph.D. Thesis, Politecnico di Torino]."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"5043","DOI":"10.1109\/TAES.2022.3219366","article-title":"A Systematic Review of Machine Learning Techniques for GNSS Use Cases","volume":"58","author":"Siemuri","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Zhao, S., Zhou, Y., and Huang, T. (2022). A Novel Method for AI-Assisted INS\/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage. Remote Sens., 14.","DOI":"10.3390\/rs14184494"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.9728\/dcs.2020.21.11.2057","article-title":"Improvement of Network RTK Positioning in Urban and Forest Land Using BeiDou","volume":"21","author":"Lee","year":"2020","journal-title":"J. Digit. Contents Soc."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1134\/S2075108720010022","article-title":"Artificial Intelligence Based Methods for Accuracy Improvement of Integrated Navigation Systems During GNSS Signal Outages: An Analytical Overview","volume":"11","author":"Gavrilov","year":"2020","journal-title":"Gyroscopy Navig."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1017\/S0373463318000760","article-title":"A Hybrid Intelligent Algorithm DGP-MLP for GNSS\/INS Integration during GNSS Outages","volume":"72","author":"Zhang","year":"2018","journal-title":"J. Navig."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Fang, W., Jiang, J., Lu, S., Gong, Y., Tao, Y., Tang, Y., Yan, P., Luo, H., and Liu, J. (2020). A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages. Remote Sens., 12.","DOI":"10.3390\/rs12020256"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"110516","DOI":"10.1016\/j.measurement.2021.110516","article-title":"A performance compensation method for GPS\/INS integrated navigation system based on CNN\u2013LSTM during GPS outages","volume":"188","author":"Zhi","year":"2021","journal-title":"Measurement"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.inffus.2019.01.004","article-title":"Seamless navigation and mapping using an INS\/GNSS\/grid-based SLAM semi-tightly coupled integration scheme","volume":"50","author":"Chiang","year":"2019","journal-title":"Inf. Fusion."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Chiang, K., Le, D.T., Duong, T.T., and Sun, R. (2020). The Performance Analysis of INS\/GNSS\/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments. Remote Sens., 12.","DOI":"10.3390\/rs12111732"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"You, B., Zhong, G., Chen, C., Li, J., and Ma, E. (2023). A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for Global Navigation Satellite System. Sensors, 23.","DOI":"10.3390\/s23136000"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Khoshelham, K., and Ramezani, M. (2017, January 6\u20138). Vehicle positioning in the absence of GNSS signals: Potential of visual-inertial odometry. Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates.","DOI":"10.1109\/JURSE.2017.7924574"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1049\/iet-rsn.2019.0004","article-title":"Adaptive cruise control radar-based positioning in GNSS challenging environment","volume":"13","author":"Abosekeen","year":"2019","journal-title":"IET Radar Sonar Navig."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Zhu, K., Guo, X., Jiang, C., Xue, Y., Li, Y., Han, L., and Chen, Y. (2020). MIMU\/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results. Sensors, 20.","DOI":"10.3390\/s20082302"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Saleh, S., Bader, Q., Karaim, M., Elhabiby, M., and Noureldin, A. (2023). Integrated 5G mmWave Positioning in Deep Urban Environments: Advantages and Challenges. arXiv.","DOI":"10.1109\/GLOBECOM54140.2023.10437537"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Chang, L., Niu, X., Liu, T., Tang, J., and Qian, C. (2019). GNSS\/INS\/LiDAR-SLAM Integrated Navigation System Based on Graph Optimization. Remote Sens., 11.","DOI":"10.3390\/rs11091009"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Abdelaziz, N., and El-Rabbany, A. (2022). An Integrated INS\/LiDAR SLAM Navigation System for GNSS-Challenging Environments. Sensors, 22.","DOI":"10.3390\/s22124327"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.procs.2015.12.336","article-title":"Sensor Technologies and Simultaneous Localization and Mapping (SLAM)","volume":"76","author":"Chong","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Gupta, H., Andreasson, H., Lilienthal, A.J., and Kurtser, P. (2023). Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors. Sensors, 23.","DOI":"10.3390\/s23104736"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Agunbiade, O.Y., and Zuva, T. (2018, January 6\u20137). Simultaneous Localization and Mapping in Application to Autonomous Robot. Proceedings of the 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), Mon Tresor, Mauritius.","DOI":"10.1109\/ICONIC.2018.8601094"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Li, J., Liu, Y., Wang, J., Yan, M., and Yao, Y. (2018, January 25\u201327). 3D Semantic Mapping Based on Convolutional Neural Networks. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8482938"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"112265","DOI":"10.1016\/j.sna.2020.112265","article-title":"Different sensor based intelligent spraying systems in Agriculture","volume":"316","author":"Abbas","year":"2020","journal-title":"Sens. Actuators A Phys."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Aldibaja, M., Suganuma, N., Yanase, R., Yoneda, K., and Cao, L. (2022, January 11\u201315). On LIDAR Map Combination: A Graph Slam Module to Generate Accurate and Largescale Maps for Autonomous Driving. Proceedings of the 2022 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Sapporo, Japan.","DOI":"10.1109\/AIM52237.2022.9863342"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.isprsjprs.2017.09.006","article-title":"Graph SLAM correction for single scanner MLS forest data under boreal forest canopy","volume":"132","author":"Kukko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Chud\u00e1, J., V\u00fdbo\u0161\u0165ok, J., Toma\u0161t\u00edk, J., Chud\u00fd, F., Tun\u00e1k, D., Skladan, M., Tu\u010dek, J., and Mokro\u0161, M. (2024). Prompt Mapping Tree Positions with Handheld Mobile Scanners Based on SLAM Technology. Land, 13.","DOI":"10.3390\/land13010093"},{"key":"ref_151","unstructured":"Bienert, A., and Maas, H. (2009). Methods for the automatic geometric registration of terrestrial laser scanner point clouds in forest stands. ISPRS Int. Arch. Photogramm. Rem. Sens. Spat. Inf. Sci., 93\u201398."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"61","DOI":"10.5194\/isprs-archives-XL-3-W4-61-2016","article-title":"Trajectory Adjustment of Mobile Laser Scan Data in GPS Denied Environments","volume":"40","author":"Schaer","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Ghosh, J., and da Silva, I. (2020). Applications of Geomatics in Civil Engineering, Springer. Lecture Notes in Civil Engineering.","DOI":"10.1007\/978-981-13-7067-0"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"012091","DOI":"10.1088\/1755-1315\/906\/1\/012091","article-title":"Influence of Control Points Configuration on the Mobile Laser Scanning Accuracy","volume":"906","author":"Kalvoda","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1080\/01431161.2017.1320451","article-title":"Extraction of road surface from mobile LiDAR data of complex road environment","volume":"38","author":"Yadav","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_156","unstructured":"McElhinney, C.P., Kumar, P., Cahalane, C., and McCarthy, T. (2010, January 21\u201324). Initial Results From European Road Safety Inspection (EURSI) Mobile Mapping Project. Proceedings of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Part 5 Commission V Symposium, Newcastle upon Tyne, UK."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/JSTARS.2014.2347276","article-title":"Learning Hierarchical Features for Automated Extraction of Road Markings From 3-D Mobile LiDAR Point Clouds","volume":"8","author":"Yu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.tust.2016.06.010","article-title":"A semi-automated method for extracting vertical clearance and cross sections in tunnels using mobile LiDAR data","volume":"59","author":"Puente","year":"2016","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1109\/LGRS.2015.2449074","article-title":"Road Boundaries Detection Based on Local Normal Saliency From Mobile Laser Scanning Data","volume":"12","author":"Wang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Balado, J., D\u00edaz-Vilari\u00f1o, L., Arias, P., and Garrido, I. (2017, January 18\u201322). Point clouds to indoor\/outdoor accessibility diagnosis. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2\/W4, 2017 ISPRS Geospatial Week 2017, Wuhan, China.","DOI":"10.5194\/isprs-annals-IV-2-W4-287-2017"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.isprsjprs.2013.01.016","article-title":"Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds","volume":"79","author":"Yang","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Zhong, M., Sui, L., Wang, Z., Yang, X., Zhang, C., and Chen, N. (2020). Recovering Missing Trajectory Data for Mobile Laser Scanning Systems. Remote Sens., 12.","DOI":"10.3390\/rs12060899"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/TVCG.2012.310","article-title":"Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid","volume":"19","author":"Tam","year":"2012","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_164","unstructured":"Bellekens, B., Spruyt, V., Berkvens, R., and Weyn, M. (2014, January 24\u201328). A survey of rigid 3d pointcloud registration algorithms. Proceedings of the AMBIENT 2014: The Fourth International Conference on Ambient Computing, Applications, Services and Technologies, Rome, Italy."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2015.12.005","article-title":"Automatic registration of large-scale urban scene point clouds based on semantic feature points","volume":"113","author":"Yang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Pan, Y., Yang, B., Liang, F., and Dong, Z. (2018, January 5\u20138). Iterative global similarity points: A robust coarse-to-fine integration solution for pairwise 3d point cloud registration. Proceedings of the 2018 International Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00030"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1109\/TCSVT.2017.2730232","article-title":"A Coarse-to-Fine Algorithm for Matching and Registration in 3D Cross-Source Point Clouds","volume":"28","author":"Huang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Choy, C.B., Dong, W., and Koltun, V. (2020, January 13\u201319). Deep Global Registration. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00259"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"He, Y., Liang, B., Yang, J., Li, S., and He, J. (2017). An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features. Sensors, 17.","DOI":"10.3390\/s17081862"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1734\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:42:10Z","timestamp":1760107330000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,14]]},"references-count":169,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101734"],"URL":"https:\/\/doi.org\/10.3390\/rs16101734","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,5,14]]}}}