Statistical and feature-based methods for mobile robot position localization

Abstract

The contribution introduces design and comparison of different-brand methods for position localization of indoor mobile robots. The both methods derive the robot relative position from structure of the working environment based on range measurements gathered by a LIDAR system. As one of the methods uses statistical description of the scene the other relies on a feature-based matching approach. The suggested localization methods have been experimentally verified and the achieved results are presented and discussed.