Hybrid Denoising Algorithm for Architectural Point Clouds Acquired with SLAM Systems

The sudden development of systems capable of rapidly acquiring dense point clouds has underscored the importance of data processing and pre-processing prior to modeling. This work presents the implementation of a denoising algorithm for point clouds acquired with LiDAR SLAM systems, aimed at optimizing data processing and the reconstruction of surveyed object geometries for graphical rendering and modeling. Implemented in a MATLAB environment, the algorithm utilizes an approximate modeling of a reference surface with Poisson’s model and a statistical analysis of the distances between the original point cloud and the reconstructed surface. Tested on point clouds from historically significant buildings with complex geometries scanned with three different SLAM systems, the results demonstrate a satisfactory reduction in point density to approximately one third of the original. The filtering process effectively removed about 50% of the points while preserving essential details, facilitating improved restitution and modeling of architectural and structural elements. This approach serves as a valuable tool for noise removal in SLAM-derived datasets, enhancing the accuracy of architectural surveying and heritage documentation.