Point-to-point Comparison Method for Automated Scan-vs-BIM Deviation Detection

Laser scanning is one of the most accurate methods of measuring the geometric accuracy of the as-built condition of a construction site. However, using laser-scanned point clouds for the purpose of measuring the deviation between the as-built structures and the as-planned Building Information Model (BIM) remains cumbersome due to the difficulty in registration, segmentation, and matching of large-scale point clouds. Conventional methods for automated deviation detection are computationally intensive and only work for regular geometric shapes such as planes and cylinders. This research proposes a point-to-point comparison method for automated scan-vs-BIM deviation detection. First, a laser-scanned point cloud is collected and imported into a BIM file. A columndetection routine is used to locate the centroid of each column in the x-y plane for both the BIM and point cloud data. The Random Sample Consensus (RANSAC) method is used to determine the optimal translation and rotation parameters to register the BIM and point cloud data. Next, the BIM model is converted to point cloud format by uniformly sampling points from each face of the building mesh model. The laser-scanned point cloud is similarly down-sampled to be at the same resolution as the BIM-derived point cloud. A point-to-point comparison sequence is carried out to measure the deviation of building elements between the BIM and laser-scanned point clouds. Regions in the point cloud are highlighted according to the degree of deviation to alert the user to the areas that require further inspection. Experiments were carried out using laser-scanned point clouds of an indoor hallway to validate the proposed approach. Results show that the proposed column-based registration method achieved a translation error rate of 0.15 meters and a rotation error rate of 0.068 degrees. The computation time required is 3 seconds for the column-based registration step and 70 seconds for the deviation detection step. The main contribution of this research is to propose a non-parametric, class-agnostic approach to deviation detection in order to handle the variation in the geometric shape of different building elements.