AMOS: comparison of scan matching approaches for self-localization in indoor environments

Abstract

This paper describes results from evaluating different self-localization approaches in indoor environments for mobile robots. The algorithms examined are based on 2D laser scans and an odometry position estimate and do not need any modifications in the environment. An important requirement for the self-localization is the ability to cope with office-like environments as well as with environments without orthogonal and rectilinear walls. Furthermore, the approaches have to be robust enough to cope with slight modifications in the daily environment and should be fast enough to be used online on board of the robot system. To fulfil these requirements we made some extensions to the existing approaches and combined them in a suitable manner. Real world experiments with our robot within the everyday environment of our institute show that the position error can be kept small enough to perform navigation tasks.