Automated digital building modelling from heterogeneous as-built data taking into account their quality characteristics - ADIBAMOD-Q

该项目的目标是为参数化关联建筑信息建模（BIM模型）开发从异构建成数据中自动生成数字建筑模型的方法。开发自动化BIM生成的挑战之一是包含来自不同来源的异构数据。建成或测量的数据包括，例如，来自摄影测量的图像，来自激光扫描仪测量的3D点云（激光雷达数据），现有的2D和3D CAD模型或2D站点平面图。因此，很明显，为了确定创建BIM模型的几何单元，必须集成不同误差级别的数据源。在许多大地测量问题中，不同数据类型与个体随机特性的集成是一项关键任务。通常，在最小二乘平差的帮助下，来自不同来源的测量数据被用于确定几何参数。用于确定单个数据组差异的算法方法，用于识别错误数据或正则化解决方案的健壮解决方案策略是大地测量学中的既定程序。这些数学方法将扩展到异质信息的处理和建筑建模的测量数据，其中随机信息将用于进一步建模过程中的结论。考虑到部分不准确、不完整甚至有缺陷的建成数据，基于3D点云的建筑建模的建议质量基础设施是提供符合ifc的BIM模型作为最终结果。工业基础类（IFC）是建筑和建筑行业的开放式标准，用于建筑模型的数字化描述。

The goal of the project is to develop methods for largely automated generation of digital building models from heterogeneous as-built data for parametric associative building information modelling (BIM model). One of the challenges in developing automated BIM generation is the inclusion of heterogeneous data from different sources. As-built or measured data includes, for example, images from photogrammetric surveys, 3D point clouds from measurements with laser scanners (LIDAR data), existing 2D and 3D CAD models or 2D site plans. It is thus clear that for the determination of geometric units for the creation of BIM models, data sources with different error levels have to be integrated. The integration of different data types with individual stochastic properties is a key task in many geodetic issues. Frequently, measurement data from different sources are used for the determination of geometric parameters with the help of a least squares adjustment. Algorithmic approaches for determining variances of individual data groups, robust solution strategies for identifying erroneous data or regularised solutions are established procedures in geodesy. These mathematical approaches are to be extended for the processing of heterogeneous information and measurement data for building modelling, whereby the stochastic information is to be used for conclusions in the further modelling process. The proposed quality infrastructure for building modelling based on 3D point clouds, taking into account partly inaccurate, incomplete or even faulty as-built data, is to provide an IFC-compliant BIM model as the final result. The Industry Foundation Classes (IFC) are an open standard in the building and construction industry for the digital description of building models.