Automatic Generation of Labeled 3D Point Clouds of Natural Environments with Gazebo.

Abstract

Progress in applying supervised learning for nat- ural scene classification is impeded by the lack of appropriate datasets for training. This paper describes the automatic generation of synthetic three-dimensional (3D) scans of natural environments with each point labelled individually with its element class. The developed software employs the robotic simulator Gazebo to obtain range and intensity measurements from a 3D laser rangefinder aboard a ground mobile robot. Precisely, the returned intensity values are used to annotate every 3D point within its corresponding class 100% error free. Several examples are provided to show the utility of the proposed approach.