Robust filtering in a laser scanner point cloud

This paper presents a new effective and robust approach to noise reduction in a three-dimensional data measurement algorithm. In the literature, there are numerous algorithms for noise reduction. The proposed filter class is based on the nonparametric estimation of the density probability function in a sliding filter sphere. The main idea of the applied algorithm depends is to maximize the distance between points in the three-dimensional space - the nearest neighbors in sliding 3D sphere.