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

根据异构竣工数据进行自动化数字建筑建模，同时考虑其质量特性 - ADIBAMOD-Q

• 批准号: 501831122
• 负责人: Professor Dr.-Ing. Frank Neitzel
• 金额: --
• 依托单位: Fachgebiet Geodäsie und Ausgleichungsrechnung
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501831122?language=en
• 关键词: Automated; digital; building; modelling; heterogeneous

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.

该项目的目标是开发方法，从参数关联建筑信息建模（BIM模型）的异构竣工数据中自动生成数字建筑模型。 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。竣工或测量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（LIDAR数据）、现有的2D和3D CAD模型或2D现场平面图。因此，很明显，为了确定用于创建BIM模型的几何单位，必须集成具有不同误差水平的数据源。 在许多大地测量问题中，具有不同随机特性的不同数据类型的集成是一项关键任务。通常，来自不同来源的测量数据用于在最小二乘平差的帮助下确定几何参数。确定单个数据组方差的数学方法，识别错误数据或正则化解决方案的鲁棒解决方案策略是大地测量学中的既定程序。这些数学方法将被扩展为异构信息和测量数据的处理，用于建筑建模，其中随机信息将用于进一步建模过程中的结论。 基于3D点云的建筑建模的拟议质量基础设施，考虑到部分不准确，不完整甚至错误的竣工数据，将提供符合IFC标准的BIM模型作为最终结果。行业基础类（IFC）是建筑和施工行业中用于建筑模型数字描述的开放标准。