Urban structure classification using the 3D normal distribution transform for practical robot applications

Abstract

Previous urban structure classification methods are intractable for practical robots in two viewpoints: storing point clouds and complex computation for using conditional random fields. This paper presents a classification method based on normal distribution transform (NDT) grids for practical robots. NDT grids store point clouds in the form of the mean and covariance, instead of directly dealing with huge point clouds. By taking the advantage of NDT grids, we design geometric-featured voxel (GFV) based on NDT grids to represent urban structures as a voxel model. The proposed method consists of three steps: GFV generation, segmentation, and classification. In the segmentation, GFVs are clustered according to units of urban structures by spectral clustering. For the classification, the clustered GFVs are classified as one kind of urban structures by supervised learning. Geometric characteristics of urban structures are expressed by a histogram of geometric words. Experimental results prove that the proposed method based on NDT grids is suitable for practical robots in terms of memory requirement, computation time, and even classification accuracy.