融合RGB-D深度图像和LiDAR点云的石油管线BIM建模

建筑信息模型(BIM)已经被广泛用于提高复杂管线系统的管理效率,但已有石油管线的BIM建模,往往依靠设计图纸或现场测绘手工完成,耗时费力。为此本文提出了一种融合RGB-D深度图像和LiDAR点云数据的石油管线BIM重建方法。首先利用RGB-D图像提供的丰富语义信息和LiDAR点云精确几何信息,对深度相机采集的RGB图像进行分割,生成三维语义地图;然后通过点云粗匹配和精确匹配实现数据融合;最后给出了不同结构管线构件的BIM模型制作方法。试验结果表明,与以往的管线BIM重建方法相比,该方法更准确、高效,有助于石油企业对含有复杂管线的计转站等实施信息化管理。

BIM modeling of oil pipeline based on RGB-D depth image and LiDAR point cloud

Building information modeling (BIM) has been widely used to improve the management efficiency of complex pipeline systems. However, BIM modeling of existing oil pipelines often relies on design drawings or field mapping, which is time-consuming and laborious. Therefore, a BIM reconstruction method for oil pipeline based on RGB-D depth image and LiDAR point cloud data is proposed. Based on the rich semantic information provided by RGB-D image and the precise geometric information of LiDAR point cloud, the RGB image collected by depth camera is segmced to generate 3D semantic map. Then data fusion is realized by point cloud rough matching and precise matching. Finally, the BIM model making method of different structure pipeline components is given. The experimental results show that compared with the previous BIM reconstruction methods of pipelines, this method is more accurate and efficient, which is helpful for petroleum enterprises to implement information management for the metering and transfer stations containing complex pipelines.