ROBUST CLASSIFICATION AND SEGMENTATION OF PLANAR AND LINEAR FEATURES FOR CONSTRUCTION SITE PROGRESS MONITORING AND STRUCTURAL DIMENSION COMPLIANCE CONTROL
ROBUST CLASSIFICATION AND SEGMENTATION OF PLANAR AND LINEAR FEATURES FOR CONSTRUCTION SITE PROGRESS MONITORING AND STRUCTURAL DIMENSION COMPLIANCE CONTROL
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DOI：
10.5194/isprsannals-ii-3-w5-129-2015
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Reza Maalek;D. Lichti;J. Ruwanpura
中科院分区：
文献类型：
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作者：
Reza Maalek;D. Lichti;J. Ruwanpura
地面激光扫描仪（TLS）在建筑工地上的应用，以自动化施工进度监测和控制结构尺寸符合性显着增长。然而，目前在施工管理中的研究依赖于规划的建筑信息模型（BIM）来将累积的点云分配给其相应的结构元素，这在竣工结构的尺寸与规划模型的尺寸不同和/或规划模型没有足够的细节的情况下可能是不可靠的。此外，由于移动物体、遮挡和灰尘造成的数据伪影，建筑工地数据集中存在离群值。为了克服上述局限性，提出了一种用于平面和线性特征的鲁棒分类和分割的新方法，以减少从建筑工地收集的LiDAR数据中存在的离群值的影响。首先，共面和共线点进行分类，通过一个强大的主成分分析程序。然后使用鲁棒聚类方法对分类点进行分组。还提出了一种方法来鲁棒地提取属于平板地板和/或天花板的点，而不执行上述阶段，以保持计算效率。该方法的适用性进行了调查，在两个场景中，即，一个实验室与30万点和一个实际的施工现场与超过1.5亿点。通过两个实验获得的结果验证了所提出的方法的适用性，在污染的数据集，如从建筑工地收集的平面和线性特征的鲁棒分割。
The application of terrestrial laser scanners (TLSs) on construction sites for automating construction progress monitoring and controlling structural dimension compliance is growing markedly. However, current research in construction management relies on the planned building information model (BIM) to assign the accumulated point clouds to their corresponding structural elements, which may not be reliable in cases where the dimensions of the as-built structure differ from those of the planned model and/or the planned model is not available with sufficient detail. In addition outliers exist in construction site datasets due to data artefacts caused by moving objects, occlusions and dust. In order to overcome the aforementioned limitations, a novel method for robust classification and segmentation of planar and linear features is proposed to reduce the effects of outliers present in the LiDAR data collected from construction sites. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a robust clustering method. A method is also proposed to robustly extract the points belonging to the flat-slab floors and/or ceilings without performing the aforementioned stages in order to preserve computational efficiency. The applicability of the proposed method is investigated in two scenarios, namely, a laboratory with 30 million points and an actual construction site with over 150 million points. The results obtained by the two experiments validate the suitability of the proposed method for robust segmentation of planar and linear features in contaminated datasets, such as those collected from construction sites.