Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning

Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by >15.79% for the SPL and by >23.83% for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/Whu-CVVMN.