An overview of the simultaneous localization and mapping on mobile robot

Localization and map building of mobile robots is a fundamental and important problem in research on robotics. This paper comprehensively surveyed the simultaneous localization and mapping (SLAM) of robot under the unknown environment. The researches of mobile robot SLAM all of the world in recent years are summarized and analyzed. The basic idea of the SLAM and the characteristics and categories of the map representation and self-location based on probability theory are detailedly analyzed and described in this paper. At the same time, the characteristics and limitations of the various research methods for SLAM are pointed out, and two typical implementing mechanisms of the SLAM are introduced.

I. INTRODUCTION

PERCEPTION of environmental features and to identify itself of the position and orientation as opposed to the global coordinates is the prerequisite and basis for autonomous navigation of mobile robots. There are many successful examples of known map for localization and navigation of mobile robot [1]. However, in most cases, the robot is working in the unknown environment. Therefore, the simultaneous localization and mapping (SLAM) of the mobile robot in unknown environment is a heated issue in the autonomous mobile robot research area. Because of its important theoretical value and application prospect, it is considered to be the key to real autonomous mobile robot. SLAM problem can be described as follows: The mobile 358 robot is placed at an unknown location in an unknown environment and incrementally build a consistent map of this environment, while simultaneously determining its location within this map [2]. The basic idea of SLAM is to use a created map to locate the localization of the mobile robot, while taking advantage of the position and orientation of the mobile robot for map building process. Therefore, localization and map building is carried out at the same time.