arXiv 1803.017452018-03cs.RO

建筑施工中集成移动机器人系统的实时的应用

Building an Integrated Mobile Robotic System for Real-Time Applications in Construction

North Carolina State University（北卡罗来纳州立大学）

Khashayar Asadi, Hariharan Ramshankar, Harish Pullagurla, Aishwarya Bhandare, Suraj Shanbhag, Pooja Mehta, Spondon Kundu, Kevin Han, Edgar Lobaton, Tianfu Wu

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摘要：

用于施工监控的实时的【形】自主机器人系统的主要挑战之一是在机器人的生命周期内同时进行定位，映射和导航，很少或没有人为干预。同时定位与地图构建（SLAM）和上下文感知是计算机视觉和机器人领域的两个活跃的研究领域。将两者实时的【形】集成到一个单一的模块化框架中进行施工监测的研究仍需进一步研究。单目视觉系统和实时的【形】场景理解在计算上是繁重的，并且主要的最先进的算法在具有高CPU和/或GPU计算能力的高端台式机和/或服务器上进行测试，这影响了它们的移动性和用于现实世界应用的部署。为了解决这些挑战并实现自动化，本文提出了一种集成的机器人计算机视觉系统，它几乎实时的【形】生成环境中存在的障碍物和可穿越空间的真实世界空间地图。这是通过将上下文感知和视觉SLAM集成到地面机器人代理中来完成的。本文介绍了上述系统的硬件利用率和性能的三种不同的室外环境，这代表了该管道的适用性，以接近实时的【形】不同的室外场景。整个系统也是独立的，不需要用户输入，这表明了这种计算机视觉系统的自主导航的潜力。

英文摘要：

One of the major challenges of a real-time autonomous robotic system for construction monitoring is to simultaneously localize, map, and navigate over the lifetime of the robot, with little or no human intervention. Past research on Simultaneous Localization and Mapping (SLAM) and context-awareness are two active research areas in the computer vision and robotics communities. The studies that integrate both in real-time into a single modular framework for construction monitoring still need further investigation. A monocular vision system and real-time scene understanding are computationally heavy and the major state-of-the-art algorithms are tested on high-end desktops and/or servers with a high CPU- and/or GPU- computing capabilities, which affect their mobility and deployment for real-world applications. To address these challenges and achieve automation, this paper proposes an integrated robotic computer vision system, which generates a real-world spatial map of the obstacles and traversable space present in the environment in near real-time. This is done by integrating contextual Awareness and visual SLAM into a ground robotics agent. This paper presents the hardware utilization and performance of the aforementioned system for three different outdoor environments, which represent the applicability of this pipeline to diverse outdoor scenes in near real-time. The entire system is also self-contained and does not require user input, which demonstrates the potential of this computer vision system for autonomous navigation.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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