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LiDAR-based place recognition relies almost exclusively on geometric information. However, geometric information may become unreliable when faced with environments dominated by unstructured objects. In this paper, we explore the role of segmentation for extracting key structured information. We propose STV-SC, a novel segmentation and temporal verification enhanced place recognition method for unstructured environments. It contains a range image-based 3D point segmentation algorithm and a three-stage process to detect a loop. The three-stage method consists of a two-stage candidate loop search process and a one-stage segmentation and temporal verification (STV) process. Our STV process utilizes the time-continuous feature of SLAM to determine whether there is an occasional mismatch. We quantitatively demonstrate that the STV process can trigger false detections caused by unstructured objects and effectively extract structured objects to avoid outliers. Comparison with state-of-art algorithms on public datasets shows that STV-SC can run online and achieve improved performance in unstructured environments (Under the same precision, the recall rate is 1.4\u223c16% higher than Scan context). Therefore, our algorithm can effectively avoid the mismatching caused by the original algorithm in unstructured environment and improve the environmental adaptability of mobile agents.<\/jats:p>","DOI":"10.3390\/s22228604","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T08:52:56Z","timestamp":1667897576000},"page":"8604","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["STV-SC: Segmentation and Temporal Verification Enhanced Scan Context for Place Recognition in Unstructured Environment"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1669-8637","authenticated-orcid":false,"given":"Xiaojie","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2494-1505","authenticated-orcid":false,"given":"Peng","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, China"},{"name":"Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8636-4168","authenticated-orcid":false,"given":"Fu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Hong Kong University, Hong Kong 999077, China"}]},{"given":"Jinlong","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, China"},{"name":"Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China"}]},{"given":"Yiguang","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, China"},{"name":"Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. 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