{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:39:13Z","timestamp":1787067553716,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,11]],"date-time":"2024-02-11T00:00:00Z","timestamp":1707609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M720433"],"award-info":[{"award-number":["2022M720433"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/s24041192","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T03:50:27Z","timestamp":1707709827000},"page":"1192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Method for Estimating Road Impulse Ahead of Vehicles in Urban Environment with Microelectromechanical System Three-Dimensional Sensor"],"prefix":"10.3390","volume":"24","author":[{"given":"Shijie","family":"Zhao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyu","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konghui","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.arcontrol.2021.03.010","article-title":"Preview-based techniques for vehicle suspension control: A state-of-the-art review","volume":"51","author":"Theunissen","year":"2021","journal-title":"Annu. Rev. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14138","DOI":"10.1109\/ACCESS.2021.3052555","article-title":"Adaptive Non-Linear Joint Probabilistic Data Association for Vehicle Target Tracking","volume":"9","author":"Zhao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1109\/LRA.2021.3060392","article-title":"Lightweight 3-D Localization and Mapping for Solid-State LiDAR","volume":"6","author":"Wang","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lin, J., and Zhang, F. (August, January 31). Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197440"},{"key":"ref_5","first-page":"1","article-title":"GEM: Online Globally Consistent Dense Elevation Mapping for Un-structured Terrain","volume":"70","author":"Pan","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zieli\u0144ski, K., and Belter, D. (August, January 31). Keyframe-based Dense Mapping with the Graph of View-Dependent Local Maps. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196865"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.measurement.2018.05.062","article-title":"Extraction of preview elevation of road based on 3D sensor","volume":"127","author":"Zhao","year":"2018","journal-title":"Measurement"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"76618","DOI":"10.1109\/ACCESS.2020.2984034","article-title":"Extraction of Preview Elevation Information Based on Terrain Mapping and Trajectory Prediction in Real-Time","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7083","DOI":"10.1109\/LRA.2022.3180439","article-title":"These Maps are Made for Walking: Real-Time Terrain Property Estimation for Mobile Robots","volume":"7","author":"Ewen","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/JRA.1987.1087096","article-title":"Sonar-based real-world mapping and navigation","volume":"3","author":"Elfes","year":"1987","journal-title":"IEEE J. Robot. Autom."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"450","DOI":"10.37394\/23203.2021.16.40","article-title":"3D environment exploration with slam for autonomous mobile robot control","volume":"16","author":"Kudriashov","year":"2021","journal-title":"WSEAS Trans. Syst. Control"},{"key":"ref_12","unstructured":"Liu, Y., Emery, R., Chakrabarti, D., Burgard, W., and Thrun, S. (July, January 28). Using EM to Learn 3D Models with Mobile Robots. Proceedings of the Eighteenth International Conference on Machine Learning (ICML), Williamstown, MA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10514-012-9321-0","article-title":"Octomap: An efficient probabilistic 3d mapping framework based on octrees","volume":"34","author":"Hornung","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1177\/0278364913499415","article-title":"3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments","volume":"32","author":"Saarinen","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Saarinen, J., Andreasson, H., Stoyanov, T., and Ala-Luhtala, J. (2013, January 6\u201310). Normal Distributions Transform Occupancy Maps: Application to large-scale online 3D mapping. Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630878"},{"key":"ref_16","unstructured":"Oleynikova, H., Millane, A., Taylor, Z., Galceran, E., Nieto, J.I., and Siegwart, R.Y. (2016, January 20\u201322). Signed distance fields: A natural representation for both mapping and planning. Proceedings of the RSS Workshop: Geometry Beyond-Representations, Physics, Scene Understanding Robot, Ann Arbor, MI, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Canelhas, D.R., Stoyanov, T., and Lilienthal, A.J. (2013, January 3\u20137). SDF Tracker: A parallel algorithm for on-line pose estimation and scene recon-struction from depth images. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696880"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sommer, C., Sang, L., Schubert, D., and Cremers, D. (2022, January 24). Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00618"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1109\/LRA.2019.2923960","article-title":"Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery","volume":"4","author":"Grinvald","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lang, R., Fan, Y., and Chang, Q. (2023). SVR-Net: A Sparse Voxelized Recurrent Network for Robust Monocular SLAM with Direct TSDF Mapping. Sensors, 23.","DOI":"10.3390\/s23083942"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1109\/LRA.2018.2849506","article-title":"Probabilistic Terrain Mapping for Mobile Robots with Uncertain Localization","volume":"3","author":"Fankhauser","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_22","unstructured":"Fankhauser, P., Bloesch, M., Gehring, C., Hutter, M., and Siegwart, R. (2014). Mobile Service Robotics, World Scientific."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1017\/S0263574715000235","article-title":"Occupancy-elevation grid: An alternative approach for robotic mapping and navigation","volume":"34","author":"Souza","year":"2016","journal-title":"Robotica"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, H., Ping, P., Shi, Q., and Chen, H. (2023). An Adaptive Two-Dimensional Voxel Terrain Mapping Method for Structured Environ-ment. Sensors, 23.","DOI":"10.3390\/s23239523"},{"key":"ref_25","unstructured":"Katyal, K., Popek, K., Paxton, C., Moore, J., and Hager, G.D. (2018). Occupancy map prediction using generative and fully convolutional networks for vehicle navigation. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sharma, V.D., Chen, J., Shrivastava, A., and Tokekar, P. (2022). Occupancy map prediction for improved indoor robot navigation. arXiv.","DOI":"10.1109\/IROS55552.2023.10341435"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8667","DOI":"10.1109\/LRA.2022.3184779","article-title":"Neural scene representation for locomotion on structured terrain","volume":"7","author":"Hoeller","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1109\/LRA.2022.3141662","article-title":"Reconstructing occluded elevation information in terrain maps with self-supervised learning","volume":"7","author":"Miki","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1109\/LRA.2022.3230325","article-title":"Real-Time Neural Dense Elevation Mapping for Urban Terrain with Uncer-tainty Estimations","volume":"8","author":"Yang","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Barranquero, M., Olmedo, A., G\u00f3mez, J., Tayebi, A., Hell\u00edn, C.J., and Saez de Adana, F. (2023). Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks. Sensors, 23.","DOI":"10.3390\/s23052444"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, J., Wang, C., Luo, W., Zhang, Y., Chang, B., and Wu, M. (2021). Research on Point Cloud Registering Method of Tunneling Roadway Based on 3D NDT-ICP Algorithm. Sensors, 21.","DOI":"10.3390\/s21134448"},{"key":"ref_32","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 Conference (RSS), Berkeley, CA, USA,.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/01691864.2015.1124025","article-title":"Grid-based scan-to-map matching for accurate 2D map building","volume":"30","author":"Ryu","year":"2016","journal-title":"Adv. Robot."},{"key":"ref_34","unstructured":"Greenspan, M., and Yurick, M. (2003, January 6\u201310). Approximate k-d tree search for efficient ICP. Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling, Banff, AB, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pomerleau, F., Breitenmoser, A., Liu, M., Colas, F., and Siegwart, R. (2012, January 11\u201313). Noise characterization of depth sensors for surface inspections. the Proceedings of the 2nd International Conference on Applied Robotics for the Power Industry (CARPI), Zurich, Switzerland.","DOI":"10.1109\/CARPI.2012.6473358"},{"key":"ref_36","first-page":"2299","article-title":"Road Profile Estimation and Preview Control for Low-Bandwidth Active Suspension Systems","volume":"20","author":"Schindler","year":"2014","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_37","unstructured":"Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A.Y. (2009, January 18\u201320). ROS: An Open-Source Robot Operating System. Proceedings of the IEEE ICRA Workshop on Open Source Software, Guiyang, China."},{"key":"ref_38","unstructured":"Koubaa, A. (2016). A Universal Grid Map Library: Implementation and Use Case for Rough Terrain Navigation, Robot Operating System (ROS)\u2014The Complete Reference, Springer International Publishing."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"095201","DOI":"10.1088\/1361-6501\/ac6ec8","article-title":"Statistical Terrain Model with Geometric Feature Detection Based on GPU Using LiDAR on Vehicles","volume":"33","author":"Liu","year":"2022","journal-title":"Meas. Sci. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:58:43Z","timestamp":1760104723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,11]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041192"],"URL":"https:\/\/doi.org\/10.3390\/s24041192","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,11]]}}}