A geometric nonlinear stochastic filter for simultaneous localization and mapping

Improved Distributed Particle Filter for Simultaneous Localization and Mapping

The Simultaneous localization and mapping (SLAM) problem have become a focus of many researches on robot navigation. Generally the most widely used filter in SLAM problems are centralized filter. It is well known that SLAM based on conventional centralized filter must reconfigure the entire state vectors when the observation dimension changes, which cause an exponential growth in computation qu...

Simultaneous localization and mapping using the Geometric Projection Filter and correspondence graph matching

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First, when no identifying marks are set on the landmarks, their identification by a robust algorithm is a complex problem which may be solved using correspondence graphs. Second, when the localization system has no a priori information about ...

Particle Filter Based Fast Simultaneous Localization and Mapping

With respect to the necessity of more autonomous capabilities for mobile robotics in ubiquitous terrains, capable methods on localization and mapping have been developed for the last decade. One of these methods is the Fast SLAM approach which is an extension of the original SLAM problem suggested. In this report we are expressing our methodology for applying a Fast SLAM method based on particl...

Simultaneous Localization and Mapping

Simultaneous Localization and Mapping

The last few years have seen the implementation of autonomous systems in industrial applications that operate in a reliable and consistent manner in areas such as stevedoring, mining, agriculture and a variety of indoor applications. To solve the localisation problem, these systems make use of either absolute information such as GPS, beacons at known positions or available maps of the environme...