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 commercial single board computer with integrated SLAM, global path planning, and motion planning. Running sensor data processing and other autonomous driving functions simultaneously, our navigation method 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 as shown in 3D simulation tests. In addition, we demonstrate the applicability of our mobile robot system through successful autonomous driving in a residential lobby.

中文翻译：

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带有RGB-D摄像头的低成本移动机器人的实时导航系统

当前，移动机器人发展迅速，并在工业中找到了许多应用。但是，仍然存在许多与它们的实际使用有关的问题，例如对昂贵的硬件的需求和它们的高功耗水平。在这项研究中，我们提出了一种导航系统，该系统可在具有RGB-D摄像头和移动机器人平台的低端计算机上运行，​​以操作集成式自动驾驶系统。提出的系统不需要LiDARs或GPU。我们的原始深度图像地面分割方法提取了可遍历性图，以安全驾驶低矮型移动机器人。它旨在通过集成了SLAM，全局路径规划和运动规划的低成本商用单板计算机来保证实时性能。同时运行传感器数据处理和其他自动驾驶功能时，我们的导航方法可以以18Hz的刷新率快速执行控制命令，而其他系统的刷新率则较慢。我们的方法优于3D模拟测试中显示的当前最先进的导航方法。此外，我们通过在住宅大厅中成功进行自动驾驶演示了我们的移动机器人系统的适用性。