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

Abstract

Progress in applying supervised learning for natural 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 range-finder 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.