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The Kalman filters have been widely used for mobile robot navigation and system integration. So that it may operate autonomously, a mobile robot must know where it is. Accurate localization is a key prerequisite for successful navigation in large-scale environments, particularly when global models are used, such as maps, drawings, topological descriptions, and CAD models. This paper presents the localization of a mobile robot using one variation of the traditional Kalman filter: the unscented Kalman filter (UKF). For this purpose the filter was implemented for a known kinematic model of the robot. [ABSTRACT FROM AUTHOR]

There has been an increasing interest for mobile robotics structures because they allow making activities without human supervision. This paper presents the main problems of an autonomous mobile robotic platform, which uses digital image processing techniques for extracting important information from the environment. In the following, there will be presented acquisition approaches based on digital cameras, approaches that will be used for visual navigation. The navigation process is based on probabilistic methods. The simultaneous localization and mapping structures will be the one that integrates both the navigation process and the probabilistic approaches. [ABSTRACT FROM AUTHOR]

The paper follows some of the progresses made in the area of vision for mobile robot indoor navigation. In this paper we deal only with indoor navigation which is further subdivided into three major categories: map-based navigation, map-building-based navigation and map-less navigation, navigation based on other techniques such as object recognition. [ABSTRACT FROM AUTHOR]