3D point cloud based hybrid maps reconstruction for indoor environments

Abstract

In this article we investigate the problem of constructing a useful 3D hybrid map for both human being and service robots in the indoor environments. The objects in our laboratory include different tables, shelves, and pillar, which are of great importance for indoor service robot. We detail the components of our map building system and explain the essential techniques. The environment is detected in 3D point clouds, after sophisticated methods operating on point cloud data removing noise points and down sampling the data, we segment the data into different clusters, estimate the posture for clusters that can be recognized from library and replace it with VRML model we built in advance, then reconstruct surface for which cannot be recognized. Finally the preliminary hybrid maps are represented with the form of point cloud, VRML model and triangular meshes in 3DMapEidtor. © 2013 Springer-Verlag.