Building Orthogonal Boundary Extraction for Airborne LiDAR Based on Directional Prediction Regularization

Extraction of building boundary is a hot issue in airborne light detection and ranging (LiDAR) point cloud data feature extraction. In order to obtain high-precision building boundary, we proposed a building orthogonal boundary regularization algorithm based on directional prediction. First, the boundary points arc extracted by-alpha shape algorithm, then the boundary key points are extracted by the improved Douglas_Peucker algorithm, the key points of angle check rules arc proposed to select the right key points, the boundary arc simplified by random sample consensus algorithm, and finally the regular boundary is got by the proposed direction prediction algorithm. The algorithm is verified by the Vaihingce data released, and the results show that, comparing with the popular classification forced orthogonal algorithm, the proposed algorithm reduces the maximum absolute deviation by an average of 43.1%, reduces the root mean square error by an average of 39. 7%, reduces the relative error of the building area by an average of 7. 02%, while increases the point cloud contribution rate by an average of 9. 32%, and it can effectively reduce the error of building orthogonal boundary regularization of airborne LiDAR point cloud.