Resumo

MUNGUIA-ALCALA, Rodrigo Francisco  e  GRAU-SALDES, Antoni. Bearing-Only SLAM: Stochastic Triangulation Method. Ing. invest. y tecnol. [online]. 2013, vol.14, n.2, pp.257-274. ISSN 2594-0732.

The SLAM or Simultaneous Localization and Mapping, is a technique in which a robot or autonomous vehicle operate in an a priori unknown environment, using only its onboard sensors to simultaneously build a map of its surroundings and use it to track its position. The sensors have a large impact on the algorithm used for SLAM. Recent approaches are focusing on the use of cameras as the main sensor, because they yield a lot of information and are well adapted for embedded systems: they are light, cheap and power saving. However, unlike range sensors which provide range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. In this work a practical method is presented, for initializing new features in bearing-only SLAM systems. The proposed method, defines a single hypothesis for the initial depth of features, by the use of an stochastic technique of triangulation. Several simulations as well two scenarios of applications with real data are presented, in order to show the performance of the proposed method.

Palavras-chave : SLAM; autonomous-vehicles; bearing-sensors; localization; mapping; robot-navigation.