RG mapping: learning compact and structured 2D line maps of indoor environments

In this paper we present region and gateway (RG) mapping, a novel approach to laser-based 2D line mapping of indoor environments. RG mapping is capable of acquiring very compact, structured, and semantically annotated maps. We present and empirically analyze the method based on map acquisition experiments with autonomous mobile robots. The experiments show that RG mapping drastically compresses the data contained in line scan maps without substantial loss of accuracy.