a survey of Simultaneous Localization And Mapping

Simultaneous Localization and Mapping (SLAM) is the process of updating a map of an unknown environment while keeping track of the position of the vehicle. In two recent decade the SLAM problem has been an active area of research in robotics. In this paper, theory for the SLAM methods, mapping, filtering, challenges and solutions are presented. Various categories of algorithms, issues and problems in SLAM introduced and their advantages and disadvantages are discussed. The filtering techniques included Bayesian filter, kalman filter, extended kalman filter and particle filter. The mapping included Location, landmark, topological, semantic, appearance and hybrid map

Keywords:

SLAM , method , mapping , problem , filtering