Three-dimensional map building for mobile robot navigation environments using a self-organizing neural network

Abstract

In recent years, mobile robots have been required to become more and more autonomous in such a way that they are able to sense and recognize the three-dimensional space in which they live or work. In this paper, we deal with such an environment map building problem from three-dimensional sensing data for mobile robot navigation. In particular, the problem to be dealt with is how to extract and model obstacles which are not represented on the map but exist in the real environment, so that the map can be newly updated using the modeled obstacle information. To achieve this, we propose a three-dimensional map building method, which is based on a self-organizing neural network technique called "growing neural gas network." Using the obstacle data acquired from the 3D data acquisition process of an active laser range finder, learning of the neural network is performed to generate a graphical structure that reflects the topology of the input space. For evaluation of the proposed method, a series of simulations and experiments are performed to build 3D maps of some given environments surrounding the robot. The usefulness and robustness of the proposed method are investigated and discussed in detail. (C) 2004 Wiley Periodicals, Inc.