  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.
