Realt-Time Spatial Information Acquisition and Use for Infrastructure Construction and Maintenance

CMS-0409326：用于基础设施建设和维护的实时空间信息获取和使用目前用于空间信息获取和建模的方法依赖于昂贵的激光距离扫描仪，这种扫描仪产生密集的点云，需要数小时或数天的后处理才能得到最终的模型。虽然这些方法生成非常详细的扫描场景的3D模型，但相关的计算时间负担排除了这些方法在现场用于实时决策的可能性。研究人员在之前的NSF支持下开发了一种用于为建筑设备操作的局部场景建模的快速建模方法，与需要计算密集型图像处理的全范围扫描相比，具有显著的优势。这一方法为开发拟议的获取、集成、建模和分析项目现场空间数据的新方法奠定了基础，这些数据包括动态现场信息(人员和设备的运动)，允许实时现场部署的可扩展性和健壮性。这些方法将能够在几秒钟内完成局部区域建模，并具有足够的精度，用于实时安全监控和先进的设备控制等应用。文中还提出了利用快速生成的场地模型开发实时避障系统，以及使用真实基础设施运行场地和设备的实验方案，以验证所提出的场地数据采集和避障方法。预计拟议办法将大大改善设备安全，同时减少熟练工人在各种现场工作条件下操作重型设备的需要。初步的实验工作证明了这种方法的可行性。在美国，建筑业占劳动力死亡人数的很大比例。美国职业安全与健康管理局(OSHA)对美国建筑业工伤死亡的研究表明，超过50%的事故主要是由重型设备的操作造成的。造成这些事故的主要原因之一是目前正在使用的重型设备缺乏安装的安全功能。通过开发一种将有效获取和实时处理外部感测场地空间信息相结合的系统，在使重型设备更安全和更有效率方面存在巨大潜力。凭借现在商用的内部传感器和计算能力来改造设备，这样的系统在技术上和经济上都是可行的。因此，拟议研究的目的是开发方法和技术，使操作员能够在杂乱的环境中更安全、更快地操作设备，即使在能见度较低的情况下，如在地下工作。研究小组提出的技术将有助于将关键类型的人为错误的影响降至最低，并有可能从根本上提高涉及重型设备的基础设施作业的安全性和效率。

CMS-0409326: Real-Time Spatial Information Acquisition and Use for Infrastructure Construction and MaintenanceABSTRACTCurrent methods for spatial information acquisition and modeling rely on expensive laser range scanners that produce dense point clouds which require hours or days of post-processing to arrive at a finished model. While these methods produce very detailed 3D models of the scanned scene, the associated computational time burden precludes these methods from being used onsite for real-time decision-making. A rapid modeling approach for modeling local scenes for construction equipment operations, developed by the investigators with previous NSF support, presents significant advantages over full range scanning that require computationally intensive image processing. This approach provided a foundation for the development of the proposed novel methods of acquiring, integrating, modeling, and analyzing project site spatial data, including dynamic site information (motion of personnel and equipment), that allow scalability and robustness for real-time field deployment. These methods will enable complete local area modeling in the order of seconds, and with sufficient accuracy for applications such as real-time safety monitoring and advanced equipment control. The development of a real-time obstacle avoidance system using rapidly generated site models is also proposed, as well as an experimental plan with real infrastructure operation sites and equipment, to validate the proposed site data acquisition and obstacle avoidance methods. It is expected that the proposed approach will result in significant equipment safety improvements while at the same time lessening the need for skilled workers to operate heavy equipment in a wide range of site working conditions. Initial experimental work has demonstrated the feasibility of this approach.The construction industry accounts for a large percentage of workforce fatalities in the United States. An Occupational Safety and Health Administration (OSHA) study of on-the-job fatalities in the construction industry in the U.S. has shown that over 50% of the accidents stemmed largely from operation of heavy equipment. One of the major causes of these accidents is the lack of safety features installed on heavy equipment that is currently in use. Great potential exists in making heavy equipment safer and more efficient by developing a system that would integrate effective acquisition and handling of externally sensed site spatial information in real-time. With the internal sensors and computing power available commercially now to retrofit equipment, such systems are technically and economically feasible. The aim of the proposed research is therefore to develop methods and technologies that would allow an operator to operate equipment more safely and at higher speeds in cluttered environments, even in situations where visibility is poor as in underground work. The technologies proposed by the research team would assist in minimizing the impact of key types of human error and will have the potential to radically improve safety and efficiency of infrastructure operations that involve heavy equipment.