{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:26:52Z","timestamp":1760236012041,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["1808085QF213"],"award-info":[{"award-number":["1808085QF213"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Supported Project in the Thirteenth Five-year Plan of Hefei Institutes of Physical Science, Chinese Academy of Sciences","award":["KP-2019-16"],"award-info":[{"award-number":["KP-2019-16"]}]},{"name":"Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province","award":["None"],"award-info":[{"award-number":["None"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle\u2019s optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes\u2019 results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.<\/jats:p>","DOI":"10.3390\/rs13204123","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T23:02:16Z","timestamp":1634252536000},"page":"4123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1751-7383","authenticated-orcid":false,"given":"Hanqi","family":"Wang","sequence":"first","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Science Island Branch of Graduate School, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiling","family":"Wang","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Anhui Engineering Laboratory for Intelligent Driving Technology and Application, Hefei 230031, China"},{"name":"Innovation Research Institute of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0941-2402","authenticated-orcid":false,"given":"Linglong","family":"Lin","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Anhui Engineering Laboratory for Intelligent Driving Technology and Application, Hefei 230031, China"},{"name":"Innovation Research Institute of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Science Island Branch of Graduate School, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Anhui Engineering Laboratory for Intelligent Driving Technology and Application, Hefei 230031, China"},{"name":"Innovation Research Institute of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huawei","family":"Liang","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Anhui Engineering Laboratory for Intelligent Driving Technology and Application, Hefei 230031, China"},{"name":"Innovation Research Institute of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.vlsi.2021.01.002","article-title":"Vulnerable objects detection for autonomous driving: A review","volume":"78","author":"Khatab","year":"2021","journal-title":"Integration"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Su, Z., Hui, Y., Luan, T.H., Liu, Q., and Xing, R. 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