A Review on Localization and Mapping Algorithm Based on Extended Kalman Filtering

Abstract

Simultaneous localization and mapping (SLAM) algorithm for mobile robots is a key problem in the field of robotics, the determination of the SLAM problems also has gained significant research momentum in recent times. And extended Kalman filter (EKF) algorithm is the most widely used algorithm in the study of SLAM problem. In this paper, the latest progress of SLAM algorithms based on EKF is surveyed, and the key techniques adopted. Firstly, the fundamental philosophy and current situation of the EKF algorithm in the study is summarized, pointed out its drawbacks and improvements of EKF. Secondly, constructed the general model of SLAM problem, expatiated the fundamental method and current situation on SLAM algorithms based on EKF. Finally, from the trend of recent study and the existence of difficult problems, we present future research trend of EKF approaches in SLAM problem.