New registration techniques to improve mobile lidar mapping accuracy

移动测绘系统(MMS)是一种三维现实捕捉系统，它通过集成导航和成像传感器从移动的车辆(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。谷歌街景和谷歌地球景观是普通民众如何使用彩信技术的最著名的例子。彩信的应用跨越了许多学科领域，包括电力线测绘、基础设施监测和工业现场建模。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，以支持知情的工程决策。该项目的重点是解决两个与行业相关的科学问题，这两个问题将提高MMS数据模型的准确性和实用性。第一种是自动MMS校准。提取测量的准确性关键取决于对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。已知定位随时间不稳定，这会影响系统精度。将开发一种使用MMS固有的对象(例如杆子)进行自动系统校准的新方法，以允许在驾驶车辆进行数据收集时求解新的定位参数。第二种是自动图像配准。通常在MMS数据捕获之后很长时间才收集站点的图像，以便执行变化检测。在这种情况下，图像可能是从智能手机采集的，其方向未知。将开发新的方法，将这些图像自动定位到已经收集的MMS数据，以便更新站点模型。

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.