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Therefore, in this article, we demonstrate the feasibility of using perception data of autonomous vehicles to replace traditionally conducted mobile mapping surveys with a case study focusing on updating a register of roadside city trees. In our experiment, we drove along a 1.3-km-long road in Helsinki to collect laser scanner data using our autonomous car platform ARVO, which is based on a Ford Mondeo hybrid passenger vehicle equipped with a Velodyne VLS-128 Alpha Prime scanner and other high-grade sensors for autonomous perception. For comparison, laser scanner data from the same region were also collected with a specially-planned high-grade mobile mapping laser scanning system. Based on our results, the diameter at breast height, one of the key parameters of city tree registers, could be estimated with a lower root-mean-square error from the perception data of the autonomous car than from the specially-planned mobile laser scanning survey, provided that time-based filtering was included in the post-processing of the autonomous perception data to mitigate distortions in the obtained point cloud. Therefore, appropriately performed post-processing of the autonomous perception data can be regarded as a viable option for keeping maps updated in road environments. However, point cloud-processing algorithms may need to be adapted for the post-processing of autonomous perception data due to the differences in the sensors and their arrangements compared to designated mobile mapping systems. We also emphasize that time-based filtering may be required in the post-processing of autonomous perception data due to point cloud distortions around objects seen at multiple times. This highlights the importance of saving the time stamp for each data point in the autonomous perception data or saving the temporal order of the data points.<\/jats:p>","DOI":"10.3390\/rs15071790","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T01:41:34Z","timestamp":1679967694000},"page":"1790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?\u2014A Case Study Surveying Roadside City Trees"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1979-9217","authenticated-orcid":false,"given":"Eric","family":"Hyypp\u00e4","sequence":"first","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"given":"Petri","family":"Manninen","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6772-9611","authenticated-orcid":false,"given":"Jyri","family":"Maanp\u00e4\u00e4","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3142-731X","authenticated-orcid":false,"given":"Josef","family":"Taher","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"given":"Paula","family":"Litkey","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4664-6221","authenticated-orcid":false,"given":"Heikki","family":"Hyyti","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3841-6533","authenticated-orcid":false,"given":"Antero","family":"Kukko","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"},{"name":"Department of Built Environment, Aalto University, School of Engineering, P.O. Box 11000, 00076 Aalto, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4796-3942","authenticated-orcid":false,"given":"Harri","family":"Kaartinen","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"given":"Eero","family":"Ahokas","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5545-0613","authenticated-orcid":false,"given":"Xiaowei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"given":"Jesse","family":"Muhojoki","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"given":"Matti","family":"Lehtom\u00e4ki","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"given":"Juho-Pekka","family":"Virtanen","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"}]},{"given":"Juha","family":"Hyypp\u00e4","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, 02150 Espoo, Finland"},{"name":"Department of Built Environment, Aalto University, School of Engineering, P.O. Box 11000, 00076 Aalto, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"key":"ref_1","unstructured":"Holland-Letz, D., K\u00e4sser, M., Kloss, B., and M\u00fcller, T. (2022, October 18). Mobility\u2019s Future: An Investment Reality Check. Available online: https:\/\/www.mckinsey.com\/industries\/automotive-and-assembly\/our-insights\/mobilitys-future-an-investment-reality-check."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"205","DOI":"10.5194\/ars-3-205-2005","article-title":"Automotive radar and lidar systems for next generation driver assistance functions","volume":"3","author":"Rasshofer","year":"2005","journal-title":"Adv. Radio Sci."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Karam, S., Vosselman, G., Peter, M., Hosseinyalamdary, S., and Lehtola, V. (2019). Design, calibration, and evaluation of a backpack indoor mobile mapping system. Remote Sens., 11.","DOI":"10.3390\/rs11080905"},{"key":"ref_5","unstructured":"Lee, G.H., Fraundorfer, F., and Pollefeys, M. (2013, January 3\u20137). Structureless pose-graph loop-closure with a multi-camera system on a self-driving car. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Elhashash, M., Albanwan, H., and Qin, R. (2022). A Review of Mobile Mapping Systems: From Sensors to Applications. Sensors, 22.","DOI":"10.3390\/s22114262"},{"key":"ref_7","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. Photogr. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Li, Z., Tan, J., and Liu, H. (2019). Rigorous boresight self-calibration of mobile and UAV LiDAR scanning systems by strip adjustment. Remote Sens., 11.","DOI":"10.3390\/rs11040442"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/J.ENG.2016.02.010","article-title":"Autonomous driving in the iCity\u2014HD maps as a key challenge of the automotive industry","volume":"2","author":"Seif","year":"2016","journal-title":"Engineering"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Al Najada, H., and Mahgoub, I. (2016, January 20\u201322). Autonomous vehicles safe-optimal trajectory selection based on big data analysis and predefined user preferences. Proceedings of the 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON.2016.7777922"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Virtanen, J.P., Kukko, A., Kaartinen, H., Jaakkola, A., Turppa, T., Hyypp\u00e4, H., and Hyypp\u00e4, J. (2017). Nationwide point cloud\u2014The future topographic core data. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6080243"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jomrich, F., Sharma, A., R\u00fcckelt, T., Burgstahler, D., and B\u00f6hnstedt, D. (2017, January 22\u201324). Dynamic Map Update Protocol for Highly Automated Driving Vehicles. Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2017), Porto, Portugal.","DOI":"10.5220\/0006279800680078"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/JAS.2017.7510736","article-title":"Internet of vehicles in big data era","volume":"5","author":"Xu","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"584","DOI":"10.3390\/rs5020584","article-title":"A voxel-based method for automated identification and morphological parameters estimation of individual street trees from mobile laser scanning data","volume":"5","author":"Wu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Forsman, M., Holmgren, J., and Olofsson, K. (2016). Tree stem diameter estimation from mobile laser scanning using line-wise intensity-based clustering. Forests, 7.","DOI":"10.3390\/f7090206"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Hu, Q., Li, H., Wang, S., and Ai, M. (2018). Evaluating carbon sequestration and PM2. 5 removal of urban street trees using mobile laser scanning data. Remote Sens., 10.","DOI":"10.3390\/rs10111759"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"69","DOI":"10.24840\/2183-0606_004.001_0006","article-title":"Perspectives to definition of big data: A mapping study and discussion","volume":"4","author":"Ylijoki","year":"2016","journal-title":"J. Innov. Manag."},{"key":"ref_19","first-page":"1","article-title":"3D data management: Controlling data volume, velocity and variety","volume":"6","author":"Laney","year":"2001","journal-title":"META Group Res. Note"},{"key":"ref_20","unstructured":"Mayer-Sch\u00f6nberger, V., and Cukier, K. (2013). Big Data: A Revolution that Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt."},{"key":"ref_21","unstructured":"Rosenzweig, J., and Bartl, M. (2015). A review and analysis of literature on autonomous driving. E-J. Mak. Innov., 1\u201357. Available online: https:\/\/michaelbartl.com\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jo, K., Kim, C., and Sunwoo, M. (2018). Simultaneous localization and map change update for the high definition map-based autonomous driving car. Sensors, 18.","DOI":"10.3390\/s18093145"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.trc.2018.02.012","article-title":"Autonomous vehicle perception: The technology of today and tomorrow","volume":"89","author":"Gruyer","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1109\/JPROC.2019.2915983","article-title":"Edge computing for autonomous driving: Opportunities and challenges","volume":"107","author":"Liu","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/00423114.2018.1492142","article-title":"Towards connected autonomous driving: Review of use-cases","volume":"57","author":"Montanaro","year":"2019","journal-title":"Veh. Syst. Dyn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.iatssr.2019.11.008","article-title":"Deep learning-based image recognition for autonomous driving","volume":"43","author":"Fujiyoshi","year":"2019","journal-title":"IATSS Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/JIOT.2020.3043716","article-title":"Computing systems for autonomous driving: State of the art and challenges","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ilci, V., and Toth, C. (2020). High definition 3D map creation using GNSS\/IMU\/LiDAR sensor integration to support autonomous vehicle navigation. Sensors, 20.","DOI":"10.3390\/s20030899"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MITS.2020.3014152","article-title":"Mapping for autonomous driving: Opportunities and challenges","volume":"13","author":"Wong","year":"2020","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"58443","DOI":"10.1109\/ACCESS.2020.2983149","article-title":"A survey of autonomous driving: Common practices and emerging technologies","volume":"8","author":"Yurtsever","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pannen, D., Liebner, M., Hempel, W., and Burgard, W. (August, January 31). How to keep HD maps for automated driving up to date. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197419"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TITS.2020.3012034","article-title":"Deep learning-based vehicle behavior prediction for autonomous driving applications: A review","volume":"23","author":"Mozaffari","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fayyad, J., Jaradat, M.A., Gruyer, D., and Najjaran, H. (2020). Deep learning sensor fusion for autonomous vehicle perception and localization: A review. Sensors, 20.","DOI":"10.3390\/s20154220"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113816","DOI":"10.1016\/j.eswa.2020.113816","article-title":"Self-driving cars: A survey","volume":"165","author":"Badue","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yeong, D.J., Velasco-Hernandez, G., Barry, J., and Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21.","DOI":"10.20944\/preprints202102.0459.v1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, X., Wang, F., Yang, B., and Zhang, W. (2021). Autonomous vehicle localization with prior visual point cloud map constraints in GNSS-challenged environments. Remote Sens., 13.","DOI":"10.3390\/rs13030506"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9961","DOI":"10.1109\/TITS.2021.3096854","article-title":"A review and comparative study on probabilistic object detection in autonomous driving","volume":"23","author":"Feng","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, M., Chen, Q., and Fu, Z. (2022). Lsnet: Learned sampling network for 3d object detection from point clouds. Remote Sens., 14.","DOI":"10.3390\/rs14071539"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/TIV.2022.3192102","article-title":"A Survey on Map-Based Localization Techniques for Autonomous Vehicles","volume":"8","author":"Chalvatzaras","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pham, Q.H., Sevestre, P., Pahwa, R.S., Zhan, H., Pang, C.H., Chen, Y., Mustafa, A., Chandrasekhar, V., and Lin, J. (August, January 31). A*3D dataset: Towards autonomous driving in challenging environments. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197385"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuScenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2020, January 13\u201319). Scalability in perception for autonomous driving: Waymo open dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., and Yang, R. (2018, January 18\u201322). The ApolloScape dataset for autonomous driving. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"ref_45","unstructured":"Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A.S., Hauswald, L., Pham, V.H., M\u00fchlegg, M., and Dorn, S. (2020). A2d2: Audi autonomous driving dataset. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1177\/03611981211057532","article-title":"From traditional to autonomous vehicles: A systematic review of data availability","volume":"2676","author":"Masello","year":"2022","journal-title":"Transp. Res. Rec."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Manninen, P., Hyyti, H., Kyrki, V., Maanp\u00e4\u00e4, J., Taher, J., and Hyypp\u00e4, J. (2022, January 23\u201327). Towards High-Definition Maps: A Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity. Proceedings of the 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.","DOI":"10.1109\/IROS47612.2022.9982050"},{"key":"ref_48","first-page":"306","article-title":"Big autonomous vehicular data classifications: Towards procuring intelligence in ITS","volume":"9","author":"Daniel","year":"2017","journal-title":"Veh. Commun."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yoo, A., Shin, S., Lee, J., and Moon, C. (2020). Implementation of a sensor big data processing system for autonomous vehicles in the C-ITS environment. Appl. Sci., 10.","DOI":"10.3390\/app10217858"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.future.2022.07.003","article-title":"Deep understanding of big geospatial data for self-driving: Data, technologies, and systems","volume":"137","author":"Wang","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1109\/TITS.2018.2815678","article-title":"Big data analytics in intelligent transportation systems: A survey","volume":"20","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_52","unstructured":"Hyypp\u00e4, J., Kukko, A., Kaartinen, H., Matikainen, L., and Lehtom\u00e4ki, M. (2018). SOHJOA-Projekti: Robottibussi Suomen Urbaaneissa Olosuhteissa, Metropolia Ammattikorkeakoulu."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Maanp\u00e4\u00e4, J., Taher, J., Manninen, P., Pakola, L., Melekhov, I., and Hyypp\u00e4, J. (2021, January 10\u201315). Multimodal end-to-end learning for autonomous steering in adverse road and weather conditions. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413109"},{"key":"ref_54","unstructured":"Maanp\u00e4\u00e4, J., Melekhov, I., Taher, J., Manninen, P., and Hyypp\u00e4, J. (2022). Proceedings of the Image Analysis and Processing\u2013ICIAP 2022: 21st International Conference, Lecce, Italy, 23\u201327 May 2022, Springer. Proceedings Part I."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Taher, J., Hakala, T., Jaakkola, A., Hyyti, H., Kukko, A., Manninen, P., Maanp\u00e4\u00e4, J., and Hyypp\u00e4, J. (2022). Feasibility of hyperspectral single photon LiDAR for robust autonomous vehicle perception. Sensors, 22.","DOI":"10.3390\/s22155759"},{"key":"ref_56","unstructured":"Velodyne (2023, March 25). VLP-16 Puck LITE, 2018. 63-9286 Rev-H Datasheet. Available online: https:\/\/www.mapix.com\/wp-content\/uploads\/2018\/07\/63-9286_Rev-H_Puck-LITE_Datasheet_Web.pdf."},{"key":"ref_57","unstructured":"Velodyne (2023, March 25). VLS-128 Alpha Puck, 2019. 63-9480 Rev-3 datasheet. Available online: https:\/\/www.hypertech.co.il\/wp-content\/uploads\/2016\/05\/63-9480_Rev-3_Alpha-Puck_Datasheet_Web.pdf."},{"key":"ref_58","unstructured":"Novatel (2023, March 25). PwrPak7-E1. Available online: https:\/\/novatel.com\/products\/receivers\/enclosures\/pwrpak7."},{"key":"ref_59","unstructured":"Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., and Ng, A. (2009, January 12\u201317). ROS: An open-source Robot Operating System. Proceedings of the ICRA Workshop on Open Source Software in Robotics, Kobe, Japan."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 13\u201319). RandLA-Net: Efficient semantic segmentation of large-scale point clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"El Issaoui, A., Feng, Z., Lehtom\u00e4ki, M., Hyypp\u00e4, E., Hyypp\u00e4, H., Kaartinen, H., Kukko, A., and Hyypp\u00e4, J. (2021). Feasibility of mobile laser scanning towards operational accurate road rut depth measurements. Sensors, 21.","DOI":"10.3390\/s21041180"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Holopainen, M., Vastaranta, M., Kankare, V., Hyypp\u00e4, H., Vaaja, M., Hyypp\u00e4, J., Liang, X., Litkey, P., Yu, X., and Kaartinen, H. (2011, January 11\u201313). The use of ALS, TLS and VLS measurements in mapping and monitoring urban trees. Proceedings of the 2011 Joint Urban Remote Sensing Event, Munich, Germany.","DOI":"10.1109\/JURSE.2011.5764711"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"11712","DOI":"10.3390\/s120911712","article-title":"Multiplatform mobile laser scanning: Usability and performance","volume":"12","author":"Kukko","year":"2012","journal-title":"Sensors"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"12814","DOI":"10.3390\/s120912814","article-title":"Benchmarking the performance of mobile laser scanning systems using a permanent test field","volume":"12","author":"Kaartinen","year":"2012","journal-title":"Sensors"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"587","DOI":"10.3390\/rs3030587","article-title":"Mapping topography changes and elevation accuracies using a mobile laser scanner","volume":"3","author":"Vaaja","year":"2011","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5238","DOI":"10.3390\/s8095238","article-title":"Retrieval algorithms for road surface modelling using laser-based mobile mapping","volume":"8","author":"Jaakkola","year":"2008","journal-title":"Sensors"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"641","DOI":"10.3390\/rs2030641","article-title":"Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data","volume":"2","author":"Jaakkola","year":"2010","journal-title":"Remote Sens."},{"key":"ref_68","unstructured":"Novatel (2023, March 25). Inertial Explorer, 2020. D18034 Version 9 brochure. Available online: https:\/\/www.amtechs.co.jp\/product\/Waypoint_D18034_v9.pdf."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TGRS.2015.2476502","article-title":"Object classification and recognition from mobile laser scanning point clouds in a road environment","volume":"54","author":"Jaakkola","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD\u201996), Portland, OR, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_72","first-page":"886","article-title":"Error analysis for circle fitting algorithms","volume":"3","author":"Chernov","year":"2009","journal-title":"Electron. J. Stat."},{"key":"ref_73","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_74","unstructured":"Pollock, D.S.G. (1993). Smoothing with Cubic Splines, Queen Mary University of London, School of Economics and Finance."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"De Boor, C. (1978). A Practical Guide to Splines, Springer.","DOI":"10.1007\/978-1-4612-6333-3"},{"key":"ref_76","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_77","doi-asserted-by":"crossref","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. (2019, January 27\u201328). Semantickitti: A dataset for semantic scene understanding of lidar sequences. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00939"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"100050","DOI":"10.1016\/j.srs.2022.100050","article-title":"Direct and automatic measurements of stem curve and volume using a high-resolution airborne laser scanning system","volume":"5","author":"Kukko","year":"2022","journal-title":"Sci. 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