An Open-Source Low-Cost Mobile Robot System with an RGB-D Camera and Efficient Real-Time Navigation Algorithm

Currently, mobile robots are developing rapidly and are finding numerous applications in industry. However, there remain a number of problems related to their practical use, such as the need for expensive hardware and their high power consumption levels. In this study, we propose a navigation system that is operable on a low-end computer with an RGB-D camera and a mobile robot platform for the operation of an integrated autonomous driving system. The proposed system does not require LiDARs or a GPU. Our raw depth image ground segmentation approach extracts a traversability map for the safe driving of low-body mobile robots. It is designed to guarantee real-time performance on a low-cost off-the-shelf single board computer with integrated SLAM, global path planning, and motion planning. We apply both rule-based and learning-based navigation policies using the traversability map. Running sensor data processing and other autonomous driving functions simultaneously, our navigation policies performs rapidly at a refresh rate of 18Hz for control command, whereas other systems have slower refresh rates. Our method outperforms current state-of-the-art navigation approaches within limited computation resources as shown in 3D simulation tests. In addition, we demonstrate the applicability of our mobile robot system through successful autonomous driving in an indoor environment. Our entire works including hardware and software are released under an open-source license (https://github.com/shinkansan/2019-UGRP-DPoom). Our detailed video is available in https://youtu.be/mf3IufUhPPM.