Research On EKF-Based Simultaneous Localization And Mapping Algorithm

The research of mobile robot involves a lot of knowledge such as intelligent control, computer science, pattern recognition and artificial intelligence.With the fast development of new technology and the wide use of advanced sensors, mobile robot has become the focus in the field of robotics and automation.Map building and localization are two essential tasks for an autonomous mobile robot's navigation. They are important foundations to realize mobile robot navigation independently. According to working environment, it can be classified into indoor mobile robot and outdoor mobile robot.This paper aims to study simultaneous localization and mapping (SLAM) algorithm in indoor environment. The simultaneous localization and mapping (SLAM) problem is firstly introduced, based on the analysis of the localization problem and the map building problem which are two key points in the navigation techniques, including its structure, characteristics, categories and so on. It analyzes several commonly used methods of the simultaneous localization and mapping (SLAM) problem. The key point research of the paper is simultaneous localization and mapping Algorithm based on extentded Kalman filter and a line-feature based SLAM algorithm is well presented in this paper. All operations required for building and maintaining this map,such as model-setting,data association,and state-updating are described and formulated.Finally, the approach of SLAM based on EKF method has been programmed and successfully tested in the simulation work.And further experiment analysis show the Simultaneous Localization and Mapping algorithm's convergence, robustness and consistence.