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Robust obstacle avoidance is one of the critical steps for successfulgoal-driven indoor navigation tasks.Due to the obstacle missing in the visualimage and the possible missed detection issue, visual image-based obstacleavoidance techniques still suffer from unsatisfactory robustness. To mitigateit, in this paper, we propose a novel implicit obstacle map-driven indoornavigation framework for robust obstacle avoidance, where an implicit obstaclemap is learned based on the historical trial-and-error experience rather thanthe visual image. In order to further improve the navigation efficiency, anon-local target memory aggregation module is designed to leverage a non-localnetwork to model the intrinsic relationship between the target semantic and thetarget orientation clues during the navigation process so as to mine the mosttarget-correlated object clues for the navigation decision. Extensiveexperimental results on AI2-Thor and RoboTHOR benchmarks verify the excellentobstacle avoidance and navigation efficiency of our proposed method. The coresource code is available at https://github.com/xwaiyy123/object-navigation.

Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance