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Herein, an event\u2010based stereo visual odometry (VO) system via adaptive time\u2010surface (TS) and truncated signed distance function (TSDF), namely, T\u2010ESVO, is proposed . The system consists of three carefully designed components, including the event processing unit, the mapping unit, and the tracking unit. Specifically, the event processing unit adopts a novel spatial\u2013temporal adaptive TS that can deal with different camera motions in various environments. The mapping unit introduces the TSDF to describe the 3D representation of environments and achieves depth estimation based on the global historical depth information contained in the environmental TSDF description. The tracking unit achieves the 6\u2010DoF pose estimation through an 3D\u20132D registration method based on the left\/right TS selection mechanism and the depth point selection mechanism. The effectiveness and robustness of the proposed system are evaluated on various datasets, and the experimental results show that T\u2010ESVO achieves good performance in both accuracy and robustness when compared with other state\u2010of\u2010the\u2010art event\u2010based stereo VO systems.<\/jats:p>","DOI":"10.1002\/aisy.202300027","type":"journal-article","created":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T06:34:34Z","timestamp":1688798074000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["T\u2010ESVO: Improved Event\u2010Based Stereo Visual Odometry via Adaptive Time\u2010Surface and Truncated Signed Distance Function"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5636-790X","authenticated-orcid":false,"given":"Zhe","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer National University of Defense Technology  Changsha 410005 China"}]},{"given":"Dianxi","family":"Shi","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC) Defense Innovation Institute  Beijing 100166 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9839-1489","authenticated-orcid":false,"given":"Ruihao","family":"Li","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC) Defense Innovation Institute  Beijing 100166 China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC) Defense Innovation Institute  Beijing 100166 China"}]},{"given":"Shaowu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer National University of Defense Technology  Changsha 410005 China"}]}],"member":"311","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3008413"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01410-2"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3062252"},{"key":"e_1_2_13_5_1","doi-asserted-by":"crossref","unstructured":"J. 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