{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T17:32:28Z","timestamp":1783618348837,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.<\/jats:p>","DOI":"10.3390\/s21206861","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T23:25:15Z","timestamp":1634513115000},"page":"6861","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements"],"prefix":"10.3390","volume":"21","author":[{"given":"Marius","family":"Dul\u0103u","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florin","family":"Oniga","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"ref_1","first-page":"50","article-title":"Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, L., Yang, J., and Kong, H. (June, January 29). Lidar-histogram for fast road and obstacle detection. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989159"},{"key":"ref_3","first-page":"491","article-title":"A Fast Ground Segmentation Method for 3D Point Cloud","volume":"13","author":"Chu","year":"2017","journal-title":"J. Inf. Process. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.robot.2016.06.007","article-title":"3D Lidar-based Static and Moving Obstacle Detection in Driving Environments","volume":"83","author":"Asvadi","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_5","unstructured":"Zhe, C., and Zijing, C. (2017, January 14\u201318). RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. Proceedings of the International Conference on Neural Information Processing, Guangzhou, China."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/JAS.2019.1911459","article-title":"Progressive LiDAR adaptation for road detection","volume":"6","author":"Zhe","year":"2019","journal-title":"IEEE CAA J. Autom. Sin."},{"key":"ref_7","unstructured":"Wang, H., Fan, R., Sun, Y., and Liu, M. (2021). Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms. IEEE Trans. Cybern., 1\u201311."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.robot.2018.11.002","article-title":"LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks","volume":"111","author":"Caltagirone","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Velas, M., Spanel, M., Hradis, M., and Herout, A. (2018, January 25\u201327). CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data. Proceedings of the 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Torres Vedras, Portugal.","DOI":"10.1109\/ICARSC.2018.8374167"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lyu, Y., Bai, L., and Huang, X. (2018, January 27\u201330). Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA. Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy.","DOI":"10.1109\/ISCAS.2018.8351244"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Caltagirone, L., Scheidegger, S., Svensson, L., and Wahde, M. (2017, January 11\u201314). Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. Proceedings of the IEEE Intelligent Vehicles Symposium, Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995848"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Klasing, K., Wollherr, D., and Buss, M. (2008, January 19\u201323). A Clustering Method for Efficient Segmentation of 3D Laser Data. Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543832"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Elseberg, J., Borrmann, D., and N\u00fcchter, A. (2011, January 27\u201329). Efficient processing of large 3D point clouds. Proceedings of the 2011 XXIII International Symposium on Information, Communication and Automation Technologies, Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/ICAT.2011.6102102"},{"key":"ref_14","first-page":"41","article-title":"Efficient Online Segmentation for Sparse 3D Laser","volume":"85","author":"Bogoslavskyi","year":"2016","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Strom, J., Richardson, A., and Olson, E. (2010, January 18\u201322). Graph-based Segmentation for Colored 3D Laser Point Clouds. Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5650459"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Meyer, G.P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., and Wellington, C.K. (2019, January 15\u201320). LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01296"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Meyer, G.P., Charland, J., Hegde, D., Laddha, A., and Vallespi-Gonzalez, C. (2019, January 16\u201317). Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00162"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., and Keutzer, K. (2018, January 21\u201325). SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O. (2019, January 15\u201320). PointPillars: Fast Encoders for Object Detection From Point Clouds. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"451","DOI":"10.3390\/electronics9030451","article-title":"Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera","volume":"9","author":"Guam","year":"2020","journal-title":"Electronics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1109\/TIV.2019.2938109","article-title":"Object Detection from a Few LIDAR Scanning Planes","volume":"4","author":"Rozsa","year":"2019","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, J.-U., and Kang, H.-B. (2018). A new 3D object pose detection method using LIDAR shape set. Sensors, 18.","DOI":"10.3390\/s18030882"},{"key":"ref_23","unstructured":"Wu, Z., Weiliang, T., Li, J., and Chi-Wing, F. (2021, January 19\u201325). SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mao, J., Niu, M., Bai, H., Liang, X., Xu, H., and Xu, C. (2021). Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. arXiv.","DOI":"10.1109\/ICCV48922.2021.00272"},{"key":"ref_25","unstructured":"Liang, Z., Zhang, M., Zhang, Z., Zhao, X., and Pu, S. (2020). RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yoo, J.H., Kim, Y., Kim, J., and Choi, J.W. (2020, January 23\u201328). 3D-CVF: Generating Joint Camera and LiDARFeatures Using Cross-View Spatial FeatureFusion for 3D Object Detection. Proceedings of the European Conference on Computer Vision (ECCV), Online.","DOI":"10.1007\/978-3-030-58583-9_43"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., and Jia, J. (2020, January 13\u201319). 3DSSD: Point-based 3D Single Stage Object Detector. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, H., Zhao, S., Zhao, W., Zhang, L., and Shen, J. (2021). One-Stage Anchor-Free 3D Vehicle Detection from LiDAR Sensors. Sensors, 21.","DOI":"10.3390\/s21082651"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Imad, M., Doukhi, O., and Lee, D.-J. (2021). Transfer Learning Based Semantic Segmentation for 3D Object Detection from Point Cloud. Sensors, 21.","DOI":"10.3390\/s21123964"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Du, J., Jiang, Z., Huang, S., Wang, Z., Su, J., Su, S., Wu, Y., and Cai, G. (2021). Point Cloud Semantic Segmentation Network Based on Multi-Scale Feature Fusion. Sensors, 21.","DOI":"10.3390\/s21051625"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Caltagirone, L., Bellone, M., Svensson, L., Wahde, M., and Sell, R. (2021). Lidar\u2013Camera Semi-Supervised Learning for Semantic Segmentation. Sensors, 21.","DOI":"10.3390\/s21144813"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Oniga, F., and Nedevschi, S. (2018, January 6\u20138). A Fast Ransac Based Approach for Computing the Orientation of Obstacles in Traffic Scenes. Proceedings of the 2018 IEEE Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP.2018.8516642"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kraemer, S., Bouzouraa, M.E., and Stiller, C. (2018, January 1\u20135). LiDAR-Based Object Tracking and Shape Estimation Using Polylines and Free-Space Information. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593385"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Hu, Q., Wu, M., Liu, J., and Wu, X. (2016). Extraction and Simplification of Building Fa\u00e7ade Pieces from Mobile Laser Scanner Point Clouds for 3D Street View Services. Int. J. Geo Inf., 5.","DOI":"10.3390\/ijgi5120231"},{"key":"ref_35","first-page":"347","article-title":"Automatic Building Facade Detection in Mobile Laser Scanner point Clouds","volume":"21","author":"Arachchige","year":"2012","journal-title":"Publ. Der. Dtsch. F\u00fcr. Photogramm. Fernerkund. Und. Geoinf. E.V."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Castagno, J., and Atkins, E. (2020). Polylidar3D-Fast Polygon Extraction from 3D Data. Sensors, 20.","DOI":"10.3390\/s20174819"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6861\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:15:26Z","timestamp":1760166926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,15]]},"references-count":37,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21206861"],"URL":"https:\/\/doi.org\/10.3390\/s21206861","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,15]]}}}