Autonomous simultaneous localization and mapping driven by Monte Carlo uncertainty maps-based navigation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2010

Autores

Fernando A. Auat Cheein (Autor) FEUP; Fernando M. Lobo Pereira (Autor) FEUP; Fernando di Sciascio (Autor) FEUP; Ricardo Carelli (Autor) Outra

Revista

Título: Knowledge Engineering Review

Vol. 28 Nº 1

Páginas: 35-57

ISSN: 0269-8889

Editora: Cambridge University Press

Indexação

Scopus

INSPEC

COMPENDEX

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Notas: Accepted in Nov.2010