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.

翻译：鲁棒性障碍规避是实现成功的目标驱动型室内导航任务的关键步骤之一。由于视觉图像中可能存在的障碍缺失以及目标漏检问题，基于视觉图像的障碍规避技术仍面临鲁棒性不足的挑战。为缓解这一问题，本文提出一种新颖的隐式障碍地图驱动的室内导航框架，该框架通过历史试错经验而非视觉图像学习隐式障碍地图。为进一步提升导航效率，设计了一种非局部目标记忆聚合模块，利用非局部网络建模导航过程中目标语义与目标方位线索之间的内在关系，从而挖掘与导航决策最相关的目标线索。在AI2-Thor和RoboTHOR基准上的大量实验结果表明，所提方法在障碍规避性能和导航效率方面均表现优异。核心源代码已公开于https://github.com/xwaiyy123/object-navigation。