{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:56:52Z","timestamp":1776401812371,"version":"3.51.2"},"reference-count":35,"publisher":"Cambridge University Press (CUP)","issue":"10","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Terrain traversability analysis is essential for realizing autonomous navigation. This paper proposes a real-time light detection and ranging (LiDAR)-based network for terrain traversability classification in off-road environments. This network incorporates a fast BEV (Bird\u2019s Eye View) feature map generation module, which performs dynamic voxelization, pillar feature encoding and scatter on point cloud, and a traversability completion module that generates accurate and dense BEV traversability maps. The network is trained with dense ground truth labels generated through offline data processing, enabling accurate and dense traversability classification of the surrounding terrain centered on the ego vehicle, with an inference speed reaching 110 + FPS. Finally, we conduct qualitative and quantitative experiments on the RELLIS-3D off-road dataset and SemanticKITTI on-road dataset, which demonstrate the efficiency and accuracy of the proposed approach.<\/jats:p>","DOI":"10.1017\/s0263574725102324","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T07:44:47Z","timestamp":1759218287000},"page":"3519-3532","source":"Crossref","is-referenced-by-count":1,"title":["A real-time LiDAR-based terrain traversability classification approach in off-road environments"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6866-9278","authenticated-orcid":false,"given":"Siyao","family":"Wu","sequence":"first","affiliation":[{"name":"Nankai University"},{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-9204","authenticated-orcid":false,"given":"Shiyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nankai University"},{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runhua","family":"Wang","sequence":"additional","affiliation":[{"name":"Nankai University"},{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xijun","family":"Zhao","sequence":"additional","affiliation":[{"name":"China North Artificial Intelligence and Innovation Research Institute"},{"name":"Collective Intelligence and Collaboration Laboratory (CIC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenshuo","family":"Liang","sequence":"additional","affiliation":[{"name":"China North Artificial Intelligence and Innovation Research Institute"},{"name":"Collective Intelligence and Collaboration Laboratory (CIC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuebo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nankai University"},{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"S0263574725102324_ref32","doi-asserted-by":"crossref","unstructured":"[32] Jiang, P. , Osteen, P. , Wigness, M. and Saripalli, S. , \u201cRELLIS-3D Dataset: Data, Benchmarks and Analysis,\u201d In: IEEE International Conference on Robotics and Automation, Xi\u00e1n, China, IEEE (2021)\u00a0pp. 1110\u20131116.","DOI":"10.1109\/ICRA48506.2021.9561251"},{"key":"S0263574725102324_ref12","doi-asserted-by":"crossref","unstructured":"[12] Ronneberger, O. , Fischer, P. and Brox, T. , \u201cU-Net: Convolutional Networks for Biomedical Image Segmentation,\u201d In: Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, Springer International Publishing (2015) pp.\u00a0234--241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"S0263574725102324_ref20","doi-asserted-by":"crossref","unstructured":"[20] Howard, A. 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Y. , Guo, J. , Ngiam, J. and Vasudevan, V. , \u201cEnd-to-end Multi-view Fusion for 3D Object Detection in LiDAR Point Clouds,\u201d In: Conference on Robot Learning, Osaka, Japan, PMLR (2020) pp. 923\u2013932."},{"key":"S0263574725102324_ref13","unstructured":"[13] Shaban, A. , Meng, X. , Lee, J. , Boots, B. and Fox, D. , \u201cSemantic Terrain Classification for Off-road Autonomous Driving,\u201d In: Conference on Robot Learning, London, UK, PMLR (2021) pp. 619--629."}],"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574725102324","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T08:27:11Z","timestamp":1763454431000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574725102324\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"references-count":35,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["S0263574725102324"],"URL":"https:\/\/doi.org\/10.1017\/s0263574725102324","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"value":"0263-5747","type":"print"},{"value":"1469-8668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,30]]}}}