Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

• Type: Another Publication in an International Scientific Journal
• Date: 2013

Authors

Fernando A A Auat Cheein (Author) Other; Fernando M L Lobo Pereira (Author) FEUP; Fernando Di Sciascio (Author) Other; Ricardo Carelli (Author) Other

Journal

Title: Knowledge Engineering Review

Vol. 28

Pages: 35-57

ISSN: 0269-8889

Publisher: Cambridge University Press

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 9 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-002-082

DOI: 10.1017/s0269888912000276

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23