New registration techniques to improve mobile lidar mapping accuracy

移动测绘系统 (MMS) 是三维现实捕捉系统，通过集成导航和成像传感器从移动车辆（地面或空中）收集地理参考空间数据。彩信数据是日常生活的一部分。 Google 街景和 Google 地球视图是普通大众如何使用 MMS 技术的最著名示例。 MMS 的应用跨越许多学科领域，包括电力线测绘、基础设施监控和工业现场建模。根据 MMS 数据构建的模型允许用户虚拟检查现场并得出测量结果，以支持明智的工程决策。该项目重点解决两个与行业相关的科学问题，这将提高 MMS 数据模型的准确性和实用性。第一个是自动 MMS 校准。提取的测量结果的准确性很大程度上取决于对 MMS 上各种传感器的相对位置和方向的精确了解，这些传感器在数据采集之前进行了校准。众所周知，随着时间的推移，方向会变得不稳定，这会影响系统的精度。将开发一种使用 MMS 固有物​​体（例如杆）进行自动系统校准的新方法，以便在车辆行驶进行数据收集时求解新的方向参数。第二个是自动图像配准。站点的图像通常是在 MMS 数据捕获后很长时间内收集的，以便执行变化检测。在这种情况下，图像可能是从智能手机收集的，并且其方向未知。我们将开发新方法，自动将这些图像定向到已收集的 MMS 数据，以便更新场地模型。

Mobile mapping systems (MMSs) are three-dimensional reality capture systems that collect georeferenced spatial data with integrated navigation and imaging sensors from a moving vehicle (terrestrial or aerial). MMS data are a part of everyday life. Google Street View and Google Earth View are the best-known examples of how MMS technology is used by the general population. The applications of MMS span many discipline areas including power line mapping, infrastructure monitoring and industrial site modelling. Models constructed from MMS data allow users to virtually inspect sites and derive measurements in support of informed engineering decisions. This project focuses on solving two industry-relevant, scientific problems that will enhance the accuracy and the utility of MMS data models. The first is automated MMS calibration. The accuracy of extracted measurements is critically dependent on precise knowledge of the relative location and orientation of the various sensors on board the MMS, which is calibrated prior to data acquisition. The orientation is known to be unstable over time, which compromises system accuracy. A new method for automated system calibration using objects inherent to an MMS (e.g. poles) will be developed to allow new orientation parameters to be solved as the vehicle is driven for data collection. The second is automated image registration. Imagery of a site is often collected long after MMS data capture in order to perform change detection. In this situation the imagery may be collected from a smart phone and its orientation is unknown. New methods will be developed to automatically orient this imagery to already-collected MMS data so that site models can be updated.