{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:36:31Z","timestamp":1760240191866,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T00:00:00Z","timestamp":1554249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51805055"],"award-info":[{"award-number":["51805055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Project","award":["2018YFB0106100"],"award-info":[{"award-number":["2018YFB0106100"]}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["the ARC Linkage Project grant LP160101081"],"award-info":[{"award-number":["the ARC Linkage Project grant LP160101081"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications.<\/jats:p>","DOI":"10.3390\/s19071614","type":"journal-article","created":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T03:13:42Z","timestamp":1554347622000},"page":"1614","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4800-6554","authenticated-orcid":false,"given":"Amirali Khodadadian","family":"Gostar","sequence":"first","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6728-5045","authenticated-orcid":false,"given":"Chunyun","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6020-4660","authenticated-orcid":false,"given":"Weiqin","family":"Chuah","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8154-2279","authenticated-orcid":false,"given":"Mohammed Imran","family":"Hossain","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8909-5728","authenticated-orcid":false,"given":"Ruwan","family":"Tennakoon","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6192-2303","authenticated-orcid":false,"given":"Alireza","family":"Bab-Hadiashar","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9525-1467","authenticated-orcid":false,"given":"Reza","family":"Hoseinnezhad","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3001, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Simultaneous Localization and Mapping: Present, Future, and the Robust-Perception Age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. 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