{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T12:23:04Z","timestamp":1769602984456,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T00:00:00Z","timestamp":1621900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T00:00:00Z","timestamp":1621900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Robot Syst"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s10846-021-01381-7","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T16:03:07Z","timestamp":1621958587000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Framework for Drivable Area Detection Via Point Cloud Double Projection on Rough Roads"],"prefix":"10.1007","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6915-5719","authenticated-orcid":false,"given":"Fengyu","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1581-304X","authenticated-orcid":false,"given":"Huawei","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6797-4123","authenticated-orcid":false,"given":"Zhiling","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0941-2402","authenticated-orcid":false,"given":"Linglong","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,25]]},"reference":[{"issue":"6","key":"1381_CR1","doi-asserted-by":"publisher","first-page":"1855","DOI":"10.1109\/TITS.2017.2742141","volume":"19","author":"S Sontges","year":"2018","unstructured":"Sontges, S., Althoff, M.: Computing the drivable area of autonomous road vehicles in dynamic road scenes. IEEE Trans. Intell. Transp. Syst. 19(6), 1855\u20131866 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1381_CR2","doi-asserted-by":"crossref","unstructured":"Haselich, M., et al.: Spline templates for fast path planning in unstructured environments. IEEE\/RSJ Int. Conf. Intell. Rob. Syst. pp. 3545\u20133550 (2011)","DOI":"10.1109\/IROS.2011.6048332"},{"issue":"4","key":"1381_CR3","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.eng.2018.07.010","volume":"4","author":"Z Liu","year":"2018","unstructured":"Liu, Z., Yu, S., Zheng, N.: A co-point mapping-based approach to drivable area detection for self-driving cars. Engineering. 4(4), 479\u2013490 (2018)","journal-title":"Engineering."},{"key":"1381_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Real-time Drivable Region Planning Based on 3D LiDAR. Int. Conf. Intell. Syst. Rem. Cont. pp. 563\u2013572 (2019)","DOI":"10.12783\/dtcse\/cisnrc2019\/33359"},{"key":"1381_CR5","doi-asserted-by":"crossref","unstructured":"Wang, H., Sun, Y., Liu, M.: Self-supervised drivable area and road anomaly segmentation using RGB-D data for robotic wheelchairs. IEEE Robot. Autom. Lett. 4(4), 4386\u20134393 (2019)","DOI":"10.1109\/LRA.2019.2932874"},{"issue":"2","key":"1381_CR6","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1109\/TVT.2013.2281199","volume":"63","author":"Q Li","year":"2013","unstructured":"Li, Q., et al.: A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios. IEEE Trans. Veh. Technol. 63(2), 540\u2013555 (2013)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"1381_CR7","unstructured":"Yang, B., Liang, M., Urtasun, R.: Hdnet: Exploiting hd maps for 3d object detection. 2th Conf. Rob. Learn, pp. 146\u2013155 (2018)"},{"key":"1381_CR8","doi-asserted-by":"crossref","unstructured":"Chang, M., et al.: Argoverse: 3d tracking and forecasting with rich maps. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 8748\u20138757 (2019)","DOI":"10.1109\/CVPR.2019.00895"},{"key":"1381_CR9","unstructured":"Liu, Z., Yu, S., Wang, X., Zheng, N.: Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach. arXiv: 1705.00451 (2017)"},{"key":"1381_CR10","unstructured":"Minaee, S., Boykov, Y., Porikli, F.: Image Segmentation Using Deep Learning: A Survey. arXiv: 2001.05566 (2020)"},{"key":"1381_CR11","doi-asserted-by":"crossref","unstructured":"Manz, M., Himmelsbach, M., Luettel, T.: Detection and tracking of road networks in rural terrain by fusing vision and LIDAR. IEEE\/RSJ Int. Conf. Intell. Rob. Syst. pp. 4562\u20134568 (2011)","DOI":"10.1109\/IROS.2011.6048167"},{"key":"1381_CR12","doi-asserted-by":"crossref","unstructured":"Grilli, E., Menna, F., Remondino, F.: A review of point clouds segmentation and classification algorithms. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XLII-2\/W3. 339\u2013344 (2017)","DOI":"10.5194\/isprs-archives-XLII-2-W3-339-2017"},{"key":"1381_CR13","doi-asserted-by":"crossref","unstructured":"Zermas, D., Izzat, I., Papanikolopoulos, N.: Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications. Proc. IEEE Int. Conf. Rob. Autom. pp. 5067\u20135073 (2017)","DOI":"10.1109\/ICRA.2017.7989591"},{"key":"1381_CR14","doi-asserted-by":"crossref","unstructured":"Rummelhard, L., et al.: Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field. Proc. IEEE Intell. Veh. Symp. pp. 1105\u20131110 (2017)","DOI":"10.1109\/IVS.2017.7995861"},{"key":"1381_CR15","doi-asserted-by":"crossref","unstructured":"Chen, T., Dai, B., Wang, R., Liu, D.: Gaussian-process-based real-time ground segmentation for autonomous land vehicles. J. Intell. Robot. Syst. Theor. Appl. 76(3), 563\u2013582 (2014)","DOI":"10.1007\/s10846-013-9889-4"},{"key":"1381_CR16","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liu, J.: Graph-based ground segmentation of 3D LIDAR in rough area. IEEE Conf. Technol. Prac. Robot Appl. pp. 1\u20135 (2014)","DOI":"10.1109\/TePRA.2014.6869157"},{"key":"1381_CR17","doi-asserted-by":"crossref","unstructured":"Byun, J., et al.: Drivable Road Detection with 3D Point Clouds Based on the MRF for Intelligent Vehicle. Springer Tracts. Adv. Rob. pp. 49\u201360 (2015)","DOI":"10.1007\/978-3-319-07488-7_4"},{"key":"1381_CR18","doi-asserted-by":"crossref","unstructured":"Gao, B., et al.: Off-Road Drivable Area Extraction Using 3D LiDAR Data. Proc. IEEE Intell. Veh. Symp. pp. 1505\u20131511 (2019)","DOI":"10.1109\/IVS.2019.8814143"},{"key":"1381_CR19","unstructured":"Roynard, X., Deschaud, J., Goulette, F.: Classification of Point Cloud Scenes with Multiscale Voxel Deep Network. arXiv: 1804.03583 (2018)"},{"key":"1381_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhou, C., Yang J., Huang K.: LiSeg: Lightweight Road-object Semantic Segmentation In 3D LiDAR Scans for Autonomous Driving. Proc. IEEE Intell. Veh. Symp. pp. 1021\u20131026 (2018)","DOI":"10.1109\/IVS.2018.8500701"},{"key":"1381_CR21","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, J., Mor\u00e1n, M., Morales, J., Reina, A., Zafra, M.: Field navigation using fuzzy elevation maps built with local 3D laser scans. Appl. Sci. 8(3), 397 (2018)","DOI":"10.3390\/app8030397"},{"key":"1381_CR22","doi-asserted-by":"crossref","unstructured":"Moosmann, F., Pink, O., Stiller, C.: Segmentation of 3d lidar data in non-flat urban environments using a local convexity criterion. Proc. IEEE Intell. Veh. Symp. pp. 215\u2013220 (2009)","DOI":"10.1109\/IVS.2009.5164280"},{"issue":"9","key":"1381_CR23","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1002\/rob.20147","volume":"23","author":"S Thrun","year":"2006","unstructured":"Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L.E., Koelen, C., Markey, C., Rummel, C., van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A., Mahoney, P.: Stanley: the robot that won the darpa grand challenge. J. Field. Rob. 23(9), 661\u2013692 (2006)","journal-title":"J. Field. Rob."},{"key":"1381_CR24","doi-asserted-by":"crossref","unstructured":"Douillard, B., et al.: Hybrid elevation maps: 3D surface models for segmentation. IEEE\/RSJ Int. Conf. Intelligent Rob. Syst. pp. 1532\u20131538 (2010)","DOI":"10.1109\/IROS.2010.5650541"},{"key":"1381_CR25","doi-asserted-by":"crossref","unstructured":"Bogoslavskyi, I., Stachniss, C., Bonn: Efficient Online Segmentation for Sparse 3D Laser Scans. PFG-J. Photogramm. Remote Sens. Geoinf. Sci. 85(1), 41\u201352 (2017)","DOI":"10.1007\/s41064-016-0003-y"},{"key":"1381_CR26","doi-asserted-by":"crossref","unstructured":"Burger, P., Wuensche, H.: Fast Multi-Pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles. Proc. IEEE Intell. Veh. Symp. pp. 2150\u20132156 (2018)","DOI":"10.1109\/IVS.2018.8500552"},{"key":"1381_CR27","doi-asserted-by":"crossref","unstructured":"Hughes, C., et al.: Drivespace: Towards context-aware drivable area detection. Intl. Symposium Electronic Imaging Science Technol. 2019(15), 42\u20131\u201342-9 (2019)","DOI":"10.2352\/ISSN.2470-1173.2019.15.AVM-042"},{"key":"1381_CR28","doi-asserted-by":"crossref","unstructured":"Guiotte, F., et al.: Semantic segmentation of LiDAR points clouds: Rasterisation beyond Digital Elevation Models. IEEE Geosci. Remote Sens. Lett. 1\u20134 (2020)","DOI":"10.1109\/LGRS.2019.2958858"},{"issue":"10","key":"1381_CR29","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.3390\/electronics8101153","volume":"8","author":"X Meng","year":"2019","unstructured":"Meng, X., Lin, Y., Yan, L., Gao, X., Yao, Y., Wang, C., Luo, S.: Airborne LiDAR point cloud filtering by a multilevel adaptive filter based on morphological reconstruction and thin plate spline interpolation. Electronics. 8(10), 1153 (2019)","journal-title":"Electronics."},{"key":"1381_CR30","doi-asserted-by":"crossref","unstructured":"Bogoslavskyi, I., Stachniss, C.: Fast range image-based segmentation of sparse 3D laser scans for online operation. IEEE Int. Conf. Intell. Rob. Syst. pp. 163\u2013169 (2016)","DOI":"10.1109\/IROS.2016.7759050"},{"issue":"3","key":"1381_CR31","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1109\/TITS.2019.2900548","volume":"21","author":"Z Luo","year":"2019","unstructured":"Luo, Z., Mohrenschildt, M., Habibi, S.: A probability occupancy grid based approach for real-time lidar ground segmentation. IEEE Trans. Intell. Transp. Syst. 21(3), 998\u20131010 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1381_CR32","unstructured":"Shan, T., Wang, J., Englot, B.: Bayesian generalized kernel inference for terrain traversability mapping. 2th Conf. Rob. Learn. pp. 829\u2013838 (2018)"},{"issue":"2","key":"1381_CR33","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/TITS.2015.2472965","volume":"17","author":"M Schreier","year":"2016","unstructured":"Schreier, M., Willert, V., Adamy, J.: Compact representation of dynamic driving environments for ADAS by parametric free space and dynamic object maps. IEEE Trans. Intell. Transp. Syst. 17(2), 367\u2013384 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1381_CR34","volume-title":"Pattern classification","author":"RO Duda","year":"2012","unstructured":"Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. John Wiley & Sons, Hoboken (2012)"},{"key":"1381_CR35","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511975837","volume-title":"Fundamentals of object tracking","author":"S Challa","year":"2011","unstructured":"Challa, S., Morelande, M.R., Musicki, D., Evans, R.J.: Fundamentals of object tracking. Cambridge University Press, Cambridge (2011)"},{"key":"1381_CR36","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. Proc IEEE Int. Conf. Comput. Vision. pp. 9297\u20139307 (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"1381_CR37","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proc IEEE Comput Soc. Conf. Comput. Vision Pattern Recognit. pp. 3354\u20133361 (2012)","DOI":"10.1109\/CVPR.2012.6248074"}],"container-title":["Journal of Intelligent & Robotic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-021-01381-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10846-021-01381-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-021-01381-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T14:30:25Z","timestamp":1672237825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10846-021-01381-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,25]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["1381"],"URL":"https:\/\/doi.org\/10.1007\/s10846-021-01381-7","relation":{},"ISSN":["0921-0296","1573-0409"],"issn-type":[{"value":"0921-0296","type":"print"},{"value":"1573-0409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,25]]},"assertion":[{"value":"15 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest\/Competing Interests"}}],"article-number":"45"}}