Building maps of large environments using splines and geometric analysis

Recently, a novel solution to the Simultaneous Localization and Map building (SLAM) problem for complex indoor environments was presented, using a set of splines for describing the geometries detected by a laser range finder mounted on a mobile platform. In this paper, a method for exploiting the geometric information underlying in these maps in the data association process is described. The proposed approach uses graphs of relations between simple features extracted from the environment, and a bit encoded implementation for obtaining a maximum clique that relates observations with previously visited areas. This information is used to update the relative positions of a collage of submaps of limited size.