Research of Mobile Robot SLAM Based on EKF and its Improved Algorithms

Abstract

Extended Kalman filter (EKF) based solution is one of the most popular techniques for solving mobile robot simultaneous localization and mapping (SLAM) problem. In this paper, the basic algorithm of EKF based SLAM and its improved algorithms are introduced. The improved algorithms are mainly on two aspects: data association and computational complexity. First, the classical data association algorithm, individual compatibility nearest neighbor (ICNN), is presented. And two improved methods including batch validation gating and multi-hypothesis are also introduced. Then, partitioned updates and submapping methods are introduced as the main ones of reducing computational complexity. Some representative improved algorithms are presented. These algorithms enable EKF to solve the mobile robot SLAM problem in cluttered and large scale environments.