Indoor Target-Driven Visual Navigation based on Spatial Semantic Information

Abstract

Target-driven visual navigation is a widely focused learningbased approach in the field of computer vision. However, it faces two major challenges: poor generalization ability to unknown scenes, and poor navigation performance for increased number of scenes. In this paper, an end-to-end target-driven visual navigation method, which uses Spatial Semantic Information (SSI) to navigate the agent to the target, is presented. To fully integrate the spatial and semantic information in the scene, visual information is encoded into an 8-D spatial context vector. In addition, the size of detected bounding box is used to improve the reward function in endto-end learning to solve the problem of sparse rewards. Experiments in interactive environment dataset AI2-THOR show that compared with state-of-the-art approaches, our approach has a higher success rate and a better route to target.