{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T05:11:37Z","timestamp":1775711497581,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSFC","award":["62103431"],"award-info":[{"award-number":["62103431"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The detection of drivable areas in off-road scenes is a challenging problem due to the presence of unstructured class boundaries, irregular features, and dust noise. Three-dimensional LiDAR data can effectively describe the terrain features, and a bird\u2019s eye view (BEV) not only shows these features, but also retains the relative size of the environment compared to the forward viewing. In this paper, a method called LRTI, which is used for detecting drivable areas based on the texture information of LiDAR reflection data, is proposed. By using an instance segmentation network to learn the texture information, the drivable areas are obtained. Furthermore, a multi-frame fusion strategy is applied to improve the reliability of the output, and a shelter\u2019s mask of a dynamic object is added to the neural network to reduce the perceptual delay caused by multi-frame fusion. Through TensorRT quantization, LRTI achieves real-time processing on the unmanned ground vehicle (UGV). The experiments on our dataset show the robustness and adaptability of LRTI to sand dust and occluded scenes.<\/jats:p>","DOI":"10.3390\/rs15010027","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T02:06:14Z","timestamp":1671674774000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Off-Road Drivable Area Detection: A Learning-Based Approach Exploiting LiDAR Reflection Texture Information"],"prefix":"10.3390","volume":"15","author":[{"given":"Chuanchuan","family":"Zhong","sequence":"first","affiliation":[{"name":"College of Intelligent Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Bowen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Intelligent Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Tao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Intelligent Science, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, B., Zhao, X., and Zhao, H. (2022). An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation. arXiv.","DOI":"10.1109\/TITS.2022.3218403"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pizzati, F., and Garc\u00eda, F. (2019, January 9\u201312). Enhanced free space detection in multiple lanes based on single CNN with scene identification. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814181"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"54","DOI":"10.2352\/ISSN.2470-1173.2017.19.AVM-021","article-title":"Free-Space detection with self-supervised and online trained fully convolutional networks","volume":"2017","author":"Sanberg","year":"2017","journal-title":"Electron. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Holder, C.J., Breckon, T.P., and Wei, X. (2016, January 11\u201314). From on-road to off: Transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46604-0_11"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2018.02.019","article-title":"Ground segmentation and free space estimation in off-road terrain","volume":"108","author":"Hamandi","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"117010","DOI":"10.1016\/j.eswa.2022.117010","article-title":"Low-latency perception in off-road dynamical low visibility environments","volume":"201","author":"Neto","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jin, Y., Han, D., and Ko, H. (\u20131, January 27). Memory-Based Semantic Segmentation for Off-road Unstructured Natural Environments. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636620"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Viswanath, K., Singh, K., Jiang, P., Sujit, P., and Saripalli, S. (2021, January 23\u201327). Offseg: A semantic segmentation framework for off-road driving. Proceedings of the 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), Lyon, France.","DOI":"10.1109\/CASE49439.2021.9551643"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sharma, S., Ball, J.E., Tang, B., Carruth, D.W., Doude, M., and Islam, M.A. (2019). Semantic segmentation with transfer learning for off-road autonomous driving. Sensors, 19.","DOI":"10.3390\/s19112577"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TITS.2013.2295427","article-title":"Combining priors, appearance, and context for road detection","volume":"15","author":"Alvarez","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TIP.2010.2045715","article-title":"General road detection from a single image","volume":"19","author":"Kong","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","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: An approach based on voxels and multi-region ground planes","volume":"83","author":"Asvadi","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hu, X., Rodriguez, F.S.A., and Gepperth, A. (2014, January 8\u201311). A multi-modal system for road detection and segmentation. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856466"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, W. (2010, January 21\u201324). Lidar-based road and road-edge detection. Proceedings of the 2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA.","DOI":"10.1109\/IVS.2010.5548134"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TRA.2004.825269","article-title":"Road-boundary detection and tracking using ladar sensing","volume":"20","author":"Wijesoma","year":"2004","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TITS.2018.2789462","article-title":"Road-segmentation-based curb detection method for self-driving via a 3D-LiDAR sensor","volume":"19","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nagy, I., and Oniga, F. (2021, January 28\u201330). Free Space Detection from Lidar Data Based on Semantic Segmentation. Proceedings of the 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP53602.2021.9733571"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Yu, Q., and Dai, J. (2022). BEVFormer: Learning Bird\u2019s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers. arXiv.","DOI":"10.1007\/978-3-031-20077-9_1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D., and Han, S. (2022). BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird\u2019s-Eye View Representation. arXiv.","DOI":"10.1109\/ICRA48891.2023.10160968"},{"key":"ref_20","unstructured":"Shaban, A., Meng, X., Lee, J., Boots, B., and Fox, D. (2022, January 14\u201318). Semantic Terrain Classification for Off-Road Autonomous Driving. Proceedings of the Conference on Robot Learning, Auckland, New Zealand."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gao, B., Xu, A., Pan, Y., Zhao, X., Yao, W., and Zhao, H. (2019, January 9\u201312). Off-road drivable area extraction using 3D LiDAR data. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814143"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2015.05.092","article-title":"Video-based road detection via online structural learning","volume":"168","author":"Yuan","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Aly, M. (2008, January 4\u20136). Real time detection of lane markers in urban streets. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621152"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TITS.2007.908582","article-title":"Robust lane detection and tracking in challenging scenarios","volume":"9","author":"Kim","year":"2008","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Uchiyama, H., Deguchi, D., Takahashi, T., Ide, I., and Murase, H. (2011, January 5\u20139). 3-D line segment reconstruction using an in-vehicle camera for free space detection. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940508"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Oana, I. (2016, January 8\u201310). Disparity image segmentation for free-space detection. Proceedings of the 2016 IEEE 12th International Conference on Intelligent Computer Communication And Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP.2016.7737150"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Neumann, L., Vanholme, B., Gressmann, M., Bachmann, A., K\u00e4hlke, L., and Sch\u00fcle, F. (2015, January 15\u201318). Free space detection: A corner stone of automated driving. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain.","DOI":"10.1109\/ITSC.2015.210"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8246","DOI":"10.1109\/TGRS.2020.2973363","article-title":"Nonlocal graph convolutional networks for hyperspectral image classification","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"Acm Trans. Graph. (tog)"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph convolutional networks for hyperspectral image classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.1109\/JSEN.2019.2952857","article-title":"A machine learning approach to road surface anomaly assessment using smartphone sensors","volume":"20","author":"Basavaraju","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1080\/10298436.2020.1809659","article-title":"Calibration of smartphone sensors to evaluate the ride quality of paved and unpaved roads","volume":"23","author":"Yang","year":"2022","journal-title":"Int. J. Pavement Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s42421-022-00061-8","article-title":"Mobile sensing for multipurpose applications in transportation","volume":"4","author":"Aboah","year":"2022","journal-title":"J. Big Data Anal. Transp."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7207","DOI":"10.1109\/JSEN.2021.3051931","article-title":"Anomalies detection through smartphone sensors: A review","volume":"21","author":"Krichen","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sattar, S., Li, S., and Chapman, M. (2018). Road surface monitoring using smartphone sensors: A review. Sensors, 18.","DOI":"10.3390\/s18113845"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Patra, S., Maheshwari, P., Yadav, S., Banerjee, S., and Arora, C. (2018, January 12\u201315). A joint 3d-2d based method for free space detection on roads. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00076"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chang, Y., Xue, F., Sheng, F., Liang, W., and Ming, A. (2022). Fast Road Segmentation via Uncertainty-aware Symmetric Network. arXiv.","DOI":"10.1109\/ICRA46639.2022.9812452"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, B., Lee, D., Lee, J.S., and Kee, S.C. (2021). Free Space Detection Using Camera-LiDAR Fusion in a Bird\u2019s Eye View Plane. Sensors, 21.","DOI":"10.3390\/s21227623"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Leung, T.H.Y., Ignatyev, D., and Zolotas, A. (2022, January 18\u201320). Hybrid Terrain Traversability Analysis in Off-road Environments. Proceedings of the 2022 8th International Conference on Automation, Robotics and Applications (ICARA), Prague, Czech Republic.","DOI":"10.1109\/ICARA55094.2022.9738557"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/JAS.2019.1911459","article-title":"Progressive lidar adaptation for road detection","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1002\/rob.21417","article-title":"Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain","volume":"29","author":"Zhou","year":"2012","journal-title":"J. Field Robot."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lei, G., Yao, R., Zhao, Y., and Zheng, Y. (2021). Detection and modeling of unstructured roads in forest areas based on visual-2D lidar data fusion. Forests, 12.","DOI":"10.3390\/f12070820"},{"key":"ref_43","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 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995848"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tang, H., Wu, T., and Dai, B. (2021, January 29\u201331). SmogNet: A point cloud smog segmentation network for unmanned vehicles. Proceedings of the 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI), Tianjin, China.","DOI":"10.1109\/CVCI54083.2021.9661231"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.isprsjprs.2018.10.006","article-title":"Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification","volume":"147","author":"Hong","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24759","DOI":"10.1109\/ACCESS.2022.3154419","article-title":"CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving","volume":"10","author":"Sharma","year":"2022","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A.Y. (2009, January 12\u201317). ROS: An open-source Robot Operating System. Proceedings of the ICRA Workshop on Open Source Software, Kobe, Japan.","DOI":"10.1109\/MRA.2010.936956"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_55","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/27\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:45:35Z","timestamp":1760147135000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010027"],"URL":"https:\/\/doi.org\/10.3390\/rs15010027","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]}}}