Simultaneous localization and mapping using a micro-particle swarm optimization

Scan matching is a popular way of calculating a robot's position given range data corresponding to objects in the environment. This paper proposes a simultaneous localization and mapping algorithm that uses micro-particle swarm optimization as an alternative method to the traditional scan matching algorithms. The effectiveness of this algorithm is tested and compared to other popular simultaneous and localization algorithms.

I. Introduction

Simultaneous localization and mapping, or SLAM, represents one of the best ways of navigating a mobile robot. In comparison, other types of navigation provide the robot with some type of external information. This could be in the form of a map of the environment, localization beacons, or GPS. With SLAM, however, there is no initial information known about the environment. All data is obtained by onboard sensors instead of from external sources. Therefore, SLAM is often used for navigating autonomous robots. A good SLAM algorithm will allow a robot to know its position in an environment as well as know the positions of objects around it. This allows the robot to safely navigate any environment.