  We present a novel approach for image-goal navigation, where an agent
navigates with a goal image rather than accurate target information, which is
more challenging. Our goal is to decouple the learning of navigation goal
planning, collision avoidance, and navigation ending prediction, which enables
more concentrated learning of each part. This is realized by four different
modules. The first module maintains an obstacle map during robot navigation.
The second predicts a long-term goal on the real-time map periodically, which
can thus convert an image-goal navigation task to several point-goal navigation
tasks. To achieve these point-goal navigation tasks, the third module plans
collision-free command sets for navigating to these long-term goals. The final
module stops the robot properly near the goal image. The four modules are
designed or maintained separately, which helps cut down the search time during
navigation and improves the generalization to previously unseen real scenes. We
evaluate the method in both a simulator and in the real world with a mobile
robot. The results in real complex environments show that our method attains at
least a $17\%$ increase in navigation success rate and a $23\%$ decrease in
navigation collision rate over some state-of-the-art models.
