3D maps representation using GNG

Abstract

Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly becomes unmanageable. In this work, we focus on 3D maps representation and we propose the use of a Growing Neural Gas (GNG) network as a 3D representation model of the input data. GNG method is able to represent the input data with a desired amount of neurons while preserving the topology of the input space. Experiments show how GNG method yields better input space adaptation than other state-of-the-art 3D map representation methods.