{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:27:23Z","timestamp":1767637643977,"version":"3.48.0"},"reference-count":35,"publisher":"Maximum Academic Press","issue":"1","license":[{"start":{"date-parts":[[2012,11,2]],"date-time":"2012-11-02T00:00:00Z","timestamp":1351814400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2013,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.<\/jats:p>","DOI":"10.1017\/s0269888912000276","type":"journal-article","created":{"date-parts":[[2012,11,2]],"date-time":"2012-11-02T10:07:10Z","timestamp":1351850830000},"page":"35-57","source":"Crossref","is-referenced-by-count":6,"title":["Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation"],"prefix":"10.48130","volume":"28","author":[{"given":"Fernando A.","family":"Auat Cheein","sequence":"first","affiliation":[]},{"given":"Fernando M. Lobo","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"di Sciascio","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"Carelli","sequence":"additional","affiliation":[]}],"member":"27968","published-online":{"date-parts":[[2012,11,2]]},"reference":[{"key":"S0269888912000276_ref14","unstructured":"Diosi A. , Kleeman L. 2005. Laser scan matching in polar coordinates with application to SLAM. In Proceedings of the International Conference on Intelligent Robots and Systems, (IROS 2005), Alberta, Canada, 3317\u20133322."},{"key":"S0269888912000276_ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2006.1638022"},{"key":"S0269888912000276_ref23","doi-asserted-by":"crossref","unstructured":"Kouzoubov K. , Austin D. 2004. Hybrid Topological\/Metric Approach to SLAM. In Proceedings of the IEEE International Conference on Robotics and Automation, 1(1), 872\u2013877. 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