Point cloud data filtering and downsampling using growing neural gas

3D sensors provide valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and downsampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how GNG method yields better input space adaptation to noisy data than other filtering and downsampling methods like Voxel Grid. It is also demonstrated how the state-of-the-art keypoint detectors improve their performance using filtered data with GNG network. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.