This work studies the problem of image-goal navigation, which entails guiding robots with noisy sensors and controls through real crowded environments. Recent fruitful approaches rely on deep reinforcement learning and learn navigation policies in simulation environments that are much simpler in complexity than real environments. Directly transferring these trained policies to real environments can be extremely challenging or even dangerous. We tackle this problem with a hierarchical navigation method composed of four decoupled modules. The first module maintains an obstacle map during robot navigation. The second one predicts a long-term goal on the real-time map periodically. The third one plans collision-free command sets for navigating to long-term goals, while the final module stops the robot properly near the goal image. The four modules are developed separately to suit the image-goal navigation in real crowded scenarios. In addition, the hierarchical decomposition decouples the learning of navigation goal planning, collision avoidance and navigation ending prediction, which cuts down the search space during navigation training and helps improve the generalization to previously unseen real scenes. We evaluate the method in both a simulator and the real world with a mobile robot. The results show that our method outperforms several navigation baselines and can successfully achieve navigation tasks in these scenarios.
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