{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:50:45Z","timestamp":1776181845928,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Research Council Discovery Projects","award":["DP180103852"],"award-info":[{"award-number":["DP180103852"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Solid-State LiDAR (SSL) takes an increasing share of the LiDAR market. Compared with traditional spinning LiDAR, SSLs are more compact, energy-efficient and cost-effective. Generally, the current study of SSL mapping is limited to adapting existing SLAM algorithms to an SSL sensor. However, compared with spinning LiDARs, SSLs are different in terms of their irregular scan patterns and limited FOV. Directly applying existing SLAM approaches on them often increase the instability of a mapping process. This study proposes a systematic design, which consists of a dual-LiDAR mapping system and a three DOF interpolated six DOF odometry. For dual-LiDAR mapping, this work uses a 2D LiDAR to enhance a 3D SSL performance on a ground vehicle platform. The proposed system takes a 2D LiDAR to preprocess the scanning field into a number of feature sections according to the curvatures on the 2D fraction. Subsequently, this section information is passed to 3D SSL for direction feature selection. Additionally, this work proposes an odometry interpolation method which uses both LiDARs to generate two separated odometries. The proposed odometry interpolation method selectively determines the appropriate odometry information to update the system state under challenging conditions. Experiments are conducted in different scenarios. The results proves that the proposed approach is able to utilise 12 times more corner features from the environment than the comparied method, thus results in a demonstrable improvement in its absolute position error.<\/jats:p>","DOI":"10.3390\/s21051773","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:39:07Z","timestamp":1614904747000},"page":"1773","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Enhancing Solid State LiDAR Mapping with a 2D Spinning LiDAR in Urban Scenario SLAM on Ground Vehicles"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0659-1369","authenticated-orcid":false,"given":"Weichen","family":"Wei","sequence":"first","affiliation":[{"name":"Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia"}]},{"given":"Bijan","family":"Shirinzadeh","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia"}]},{"given":"Rohan","family":"Nowell","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4649-650X","authenticated-orcid":false,"given":"Mohammadali","family":"Ghafarian","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8737-1001","authenticated-orcid":false,"given":"Mohamed M. A.","family":"Ammar","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1998-3380","authenticated-orcid":false,"given":"Tianyao","family":"Shen","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","first-page":"407","article-title":"A Flexible and Scalable SLAM System with Full 3D Motion Estimation","volume":"32","author":"Kohlbrecher","year":"2011","journal-title":"Rapid Commun. Mass Spectrom."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TRO.2006.889486","article-title":"Improved techniques for grid mapping with Rao-Blackwellized particle filters","volume":"23","author":"Grisetti","year":"2007","journal-title":"IEEE Trans. Robot."},{"key":"ref_3","first-page":"141","article-title":"LOAM: Lidar Odometry and Mapping in Real- time","volume":"32","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018). LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain. IEEE Int. Conf. Intell. Robot. Syst., 4758\u20134765.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_5","unstructured":"K\u00fchn, M., Koch, R., Fees, M., and May, S. (2015, January 3). Benchmarking the pose accuracy of different SLAM approaches for rescue robotics. Proceedings of the Applied Research Conference, N\u00fcrnberg, Germany."},{"key":"ref_6","first-page":"44","article-title":"A Critical Comparison between Fast and Hector SLAM Algorithms","volume":"3","author":"Eliwa","year":"2017","journal-title":"REST J. Emerg. Trends Model. Manuf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MITS.2010.939925","article-title":"A tutorial on graph-based SLAM","volume":"2","author":"Grisetti","year":"2010","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1109\/JSTARS.2018.2812796","article-title":"Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform","volume":"11","author":"Ravi","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","unstructured":"Jiao, J., Ye, H., Zhu, Y., and Liu, M. (2020). Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sualeh, M., and Kim, G.W. (2019). Dynamic Multi-LiDAR based multiple object detection and tracking. Sensors, 19.","DOI":"10.3390\/s19061474"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, T., and Park, T. (2017, January 19\u201322). Calibration method between dual 3D lidar sensors for autonomous vehicles. Proceedings of the 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017, Kanazawa, Japan.","DOI":"10.23919\/SICE.2017.8105583"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.robot.2009.09.011","article-title":"6D scan registration using depth-interpolated local image features","volume":"58","author":"Andreasson","year":"2010","journal-title":"Robot. Auton. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cai, H., Pang, W., Chen, X., Wang, Y., and Liang, H. (2020). A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration. Sensors, 20.","DOI":"10.3390\/s20041130"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Willis, A.R., Zapata, M.J., and Conrad, J.M. (2009, January 21\u201323). A linear method for calibrating LIDAR-and-camera systems. Proceedings of the 2009 IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems, London, UK.","DOI":"10.1109\/MASCOT.2009.5366801"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.robot.2016.05.010","article-title":"Self calibration of multiple LIDARs and cameras on autonomous vehicles","volume":"83","author":"Pereira","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1109\/TPAMI.1987.4767965","article-title":"Least-Squares Fitting of Two 3-D Point Sets","volume":"PAMI-9","author":"Arun","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.imavis.2003.09.004","article-title":"Robust registration of 2D and 3D point sets","volume":"21","author":"Fitzgibbon","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/BF01427149","article-title":"Iterative point matching for registration of free-form curves and surfaces","volume":"13","author":"Zhang","year":"1994","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Censi, A. (2008, January 19\u201323). An ICP variant using a point-to-line metric. Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543181"},{"key":"ref_20","unstructured":"Heide, N., Emter, T., and Petereit, J. (2018, January 20\u201321). Calibration of multiple 3D LiDAR sensors to a common vehicle frame. Proceedings of the 50th International Symposium on Robotics, Munich, Germany."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jiao, J., Yu, Y., Liao, Q., Ye, H., and Liu, M. (2019, January 3\u20138). Automatic calibration of multiple 3D LiDARs in urban environments. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967797"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/978-3-642-28572-1_12","article-title":"Automatic self-calibration of a full field-of-view 3D n-Laser scanner","volume":"79","author":"Sheehan","year":"2014","journal-title":"Springer Tracts Adv. Robot."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sommer, H., Khanna, R., Gilitschenski, I., Taylor, Z., Siegwart, R., and Nieto, J. (2017, January 24\u201328). A low-cost system for high-rate, high-accuracy temporal calibration for LIDARs and cameras. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206042"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Walters, C., Mendez, O., Hadfield, S., and Bowden, R. (2019, January 3\u20138). A Robust Extrinsic Calibration Framework for Vehicles with Unscaled Sensors. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Macau, China.","DOI":"10.1109\/IROS40897.2019.8968244"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, W., Shirinzadeh, B., Ghafarian, M., Esakkiappan, S., and Shen, T. (2020, January 6\u201310). Hector SLAM with ICP trajectory matching. Proceedings of the IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, Boston, MA, USA.","DOI":"10.1109\/AIM43001.2020.9158946"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bosse, M., and Zlot, R. (2009, January 12\u201317). Continuous 3D scan-matching with a spinning 2D laser. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152851"},{"key":"ref_27","unstructured":"Yang, Y., Yang, G., Tian, Y., Zheng, T., Li, L., and Wang, Z. (June, January 31). A robust and accurate SLAM algorithm for omni-directional mobile robots based on a novel 2.5D lidar device. Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, Wuhan, China."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3715129","DOI":"10.1155\/2016\/3715129","article-title":"Calibration of Short Range 2D Laser Range Finder for 3D SLAM Usage","volume":"2016","author":"Olivka","year":"2016","journal-title":"J. Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 12\u201316). LOAM: Lidar Odometry and Mapping in Real-time. Proceedings of the Robotics: Science and Systems, Berkeley, CA, USA.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pfrunder, A., Borges, P.V.K., Romero, A.R., Catt, G., and Elfes, A. (2017, January 24\u201328). Real-Time Autonomous Ground Vehicle Navigation in Heterogeneous Environments Using a 3D LiDAR. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206083"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Baldwin, I., and Newman, P. (2012, January 7\u201312). Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors. Proceedings of the IEEE International Conference on Intelligent Robots and Systems Vilamoura, Portugal.","DOI":"10.1109\/IROS.2012.6385677"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, S., Wang, D., and Xu, S. (2016, January 9\u201311). 3D indoor modeling using a hand-held embedded system with multiple laser range scanners. Proceedings of the International Symposium on Optoelectronic Technology and Application 2016, Beijing, China.","DOI":"10.1117\/12.2247006"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chong, Z.J., Qin, B., Bandyopadhyay, T., Ang, M.H., Frazzoli, E., and Rus, D. (2013, January 6\u201310). Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment. Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630777"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, L., Zhao, J., He, X., and Ye, C. (July, January 26). DLO: Direct LiDAR odometry for 2.5D outdoor environment. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500639"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jiang, G., Yin, L., Jin, S., Tian, C., Ma, X., and Ou, Y. (2019). A simultaneous localization and mapping (SLAM) framework for 2.5D map building based on low-cost LiDAR and vision fusion. Appl. Sci., 9.","DOI":"10.3390\/app9102105"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Coughlan, J.M. (1999, January 20\u201327). Manhattan World: Compass Direction from a Single Image by Bayesian Inference 2 Previous Work and Three- Dimen- sional Geometry. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790349"},{"key":"ref_37","unstructured":"Coughlan, J.M., and Yuille, A.L. (2001). The manhattan world assumption: Regularities in scene statistics which enable Bayesian inference. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shankar, K.S., and Harmon, T.L. (1986). Introduction to Robotics, Addison-Wesley.","DOI":"10.1109\/MEX.1986.4306961"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4091","DOI":"10.1364\/OL.42.004091","article-title":"Coherent solid-state LIDAR with silicon photonic optical phased arrays","volume":"42","author":"Poulton","year":"2017","journal-title":"Opt. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1007\/s00502-018-0635-2","article-title":"MEMS-based lidar for autonomous driving","volume":"135","author":"Yoo","year":"2018","journal-title":"Elektrotechnik Informationstechnik"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.patrec.2017.09.038","article-title":"Multimodal vehicle detection: Fusing 3D-LIDAR and color camera data","volume":"115","author":"Asvadi","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, J., and Zhang, F. (2019, January 20\u201324). Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV. Proceedings of the IEEE International Conference on Robotics and Automation, Montreal, QC, Montreal.","DOI":"10.1109\/ICRA40945.2020.9197440"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2918","DOI":"10.1109\/LRA.2019.2923381","article-title":"Sparse depth enhanced direct thermal-infrared SLAM beyond the visible spectrum","volume":"4","author":"Shin","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sabatini, S., Carno, M., Fiorenti, S., and Savaresi, S.M. (2018, January 21\u201324). Improving Occupancy Grid Mapping via Dithering for a Mobile Robot Equipped with Solid-State LiDAR Sensors. Proceedings of the 2018 IEEE Conference on Control Technology and Applications, Copenhagen, Denmark.","DOI":"10.1109\/CCTA.2018.8511318"},{"key":"ref_45","first-page":"5","article-title":"ROS: An open-source Robot Operating System","volume":"3","author":"Quigley","year":"2009","journal-title":"Icra"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1773\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:32:39Z","timestamp":1760160759000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21051773"],"URL":"https:\/\/doi.org\/10.3390\/s21051773","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]}}}