Method of Classification the Mobile Robot Workspace Based on the Analysis of a 3D Point Cloud

Abstract

One of the main and most difficult tasks in the development of automotive systems is the classification of the workspace of a mobile robot. Based on the classification results, a local map of the area is built, with the help of which, then, the robot trajectory is planned. The article describes a method of classification the working area of an autonomous mobile robot moving in rough terrain. The developed classification method is based on the analysis of a three-dimensional point cloud obtained by a laser scan- ning 3D rangefinder. Using a scanning laser rangefinder allows you to classify the robot  s motion zone at any time of the day or year. A set of classification features is proposed, the calculation of which is carried out using the least square method and elements of probability theory and mathematical statistics. The classification of the robot workspace is carried out by four classes: "Flat surface", "Small roughness", "Large roughness" and "Obstacle". Each class characterizes the degree of passability of the robot movement surface. Classification results are saved as a local map of passability. In each cell of such a map a number is written that characterizes the passability of the region of the working area bounded by this cell. The developed classifier is integrated into the on-board control system of the wheeled mobile robot. The results of experimental studies confirming the efficiency and effectiveness of the proposed classification method are presented. The accuracy of class recognition of the mobile robot workspace has been determined. The developed classifier successfully operates in various conditions, including in winter and at dusk, but at the same time it has limitations when working in conditions of natural noise, such as rain, snow. The average classification accuracy with the minimal influence of natural noise is 92.3 %, and the execution time of each iteration does not exceed 0.085 s, which allows using developed classifier as a part of on-board control systems of autonomous mobile robots.