Probabilistic Model of Spatial Concepts Integrating Generative Adversarial Networks for Semantic Mapping

Abstract

This paper proposes SpCoMapGAN, a method to generate the semantic map in a newly visited environment by training an inference model using previously estimated semantic maps. SpCoMapGAN uses generative adversarial networks (GANs) to transfer semantic information based on room arrangements to the newly visited environment. We experimentally show in simulation that SpCoMapGAN can use global information for estimating the semantic map and is superior to previous related methods.