Multi goals and multi scenes visual mapless navigation in indoor using meta-learning and scene priors

Abstract

Abstract The goal of visual mapless navigation is to navigate from a random starting point in a scene to a specified target in an unknown environment. A fundamental challenge in visual mapless navigation is generalizing to a novel environment, where the layout of the scenes and appearance of targets are unfamiliar. Furthermore, traditional navigation models are frozen during inference resulting in poor adaptability. To address these issues, we propose a multi goals and multi scenes visual mapless navigation model, which integrate meta learning with spatial relationships between different object categories. In this way, our method not only improves the generalization on multi goals in multi scenes but also encourages effective navigation. Experimental results on AI2-THOR dataset show that our approach significantly outperforms the state-of-the-art model SAVN by > 27.05 % for the average success rate and by > 31.7 % for the average SPL. Our source code and data of this paper are available at: https://github.com/zhiyu-tech/WHU-VMN.