Robust obstacle avoidance is one of the critical steps for successful goal-driven indoor navigation tasks.Due to the obstacle missing in the visual image and the possible missed detection issue, visual image-based obstacle avoidance techniques still suffer from unsatisfactory robustness. To mitigate it, in this paper, we propose a novel implicit obstacle map-driven indoor navigation framework for robust obstacle avoidance, where an implicit obstacle map is learned based on the historical trial-and-error experience rather than the visual image. In order to further improve the navigation efficiency, a non-local target memory aggregation module is designed to leverage a non-local network to model the intrinsic relationship between the target semantic and the target orientation clues during the navigation process so as to mine the most target-correlated object clues for the navigation decision. Extensive experimental results on AI2-Thor and RoboTHOR benchmarks verify the excellent obstacle avoidance and navigation efficiency of our proposed method. The core source code is available at https://github.com/xwaiyy123/object-navigation.

本文提出了一种新的基于隐式障碍图的室内导航框架，其中根据历史试错经验而非视觉图像学习隐式障碍图，同时设计了非局部目标记忆聚合模块以提高导航效率。实验结果表明，我们的方法在AI2-Thor和RoboTHOR基准测试中具有卓越的障碍避免和导航效率。

基于隐式障碍地图的室内导航模型，用于稳健的障碍物避让