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.
翻译:目前,移动机器人正在迅速发展,并正在工业中找到许多应用,然而,在实际使用方面仍然存在一些问题,例如需要昂贵的硬件和高电能消耗水平。在本研究中,我们提议建立一个在低端计算机上操作的导航系统,配有 RGB-D 相机和一个用于集成自主驾驶系统的移动机器人平台。拟议的系统不需要LIDARs 或GPU。我们的原始深度图像地面分割法为低机移动机器人的安全驾驶绘制了可移动性图。它的设计是为了保证低成本商用单机计算机的实时性能,并配有综合的SLAM、全球路径规划和运动规划。同时运行传感器数据处理和其他自主驾驶功能,我们的导航方法以18Hz的更新速度快速运行,而其他系统则使用较慢的更新速度。我们的方法比3D模拟试验中显示的当前最先进的导航方法要快。此外,我们通过在住宅大厅成功自主驾驶来展示我们移动机器人系统的实用性。