Abstract
Self-localization is one of the most important process for autonomous mobile robots. Map-based self-localization is a promising approach to achieve a precise self-localization. This method requires that the robot recognizes geometric surrounding environment accurately and obtains the correct position and attitude from the information based on the geometric data. To realize this process appropriately, we propose a method of self-localization using a combination of a 2D map and a 3D map with consideration for computational cost. Our method mainly consists of our own 3D range finding system, the Iterative Closest Point algorithm, and the Monte Calro Localization algorithm. This paper presents the process of creating the 3D map based on the 2D map created beforehand, the process of self-localizing through 2D map and 3D map, and the preliminary experiments of a series of those processes application.
