Classification within indoor environments using 3D perception

Abstract

Making sense out of human indoor environments is an essential feature for robots. In this paper we present a system for the classification of components inside these environments, starting from our robotic platform to a simple yet robust labeling process. Our method starts by acquiring multiple point clouds which are then registered into one single dataset. An estimation of principle axes is performed and the planar surfaces are segmented out. Further on, quadrilateral-like shapes are estimated for each detected plane, by making use of edges. And finally, since our classification approach relies on physical features, the method analyses the relationship between the previously mentioned shapes, as well as their physical sizes. To validate our approach, we tested the method on different datasets, which were recorded inside our office environment.