Topological Gaussian ARAM for Simultaneous Localization and Mapping (SLAM)

Abstract

This paper proposes a new neural architecture called Topological Gaussian ARAM (TGARAM) for Simultaneous Localization and Mapping (SLAM). TGARAM is integrating the Gaussian classifier with the incremental topology-learning mechanisms of the Growing Neural Gas (GNG) model for online learning of multidimensional inputs and topological map building. By using the Gaussian classifier, the sensitivity to noise on a number of benchmarks data sets is diminished, and it learns a more efficient internal representation of a mapping. The incremental topology-learning mechanisms of GNG enable TGARAM to connect the generated nodes and build a topology-preserving map. In addition, TGARAM retains multi-channel ARAM network architecture and thus capable to learn multiple mappings simultaneously across multi-modal input patterns, in an online and incremental manner. Multiple sensory sources can be transmitted to TGARAM to build a topological map and improve the estimation of localization, in order to be as generic as possible. The proposed method enables an autonomous agent to perform SLAM in an unknown environment. Finally, we validate the proposed method, through several experiments with several benchmark datasets.