  The task that requires an agent to navigate to a given object through only
visual observation is called visual object navigation (VON). The main
bottlenecks of VON are strategies exploration and prior knowledge exploitation.
Traditional strategies exploration ignores the differences of searching and
navigating stages, using the same reward in two stages, which reduces
navigation performance and training efficiency. Our study enables the agent to
explore larger area in searching stage and seek the optimal path in navigating
stage, improving the success rate of navigation. Traditional prior knowledge
exploitation focused on learning and utilizing object association, which
ignored the depth and obstacle information in the environment. This paper uses
the RGB and depth information of the training scene to pretrain the feature
extractor, which improves navigation efficiency. The obstacle information is
memorized by the agent during the navigation, reducing the probability of
collision and deadlock. Depth, obstacle and other prior knowledge are
concatenated and input into the policy network, and navigation actions are
output under the training of two-stage rewards. We evaluated our method on
AI2-Thor and RoboTHOR and demonstrated that it significantly outperforms
state-of-the-art (SOTA) methods on success rate and navigation efficiency.
