  Recent results suggest that splitting topological navigation into
robot-independent and robot-specific components improves navigation performance
by enabling the robot-independent part to be trained with data collected by
robots of different types. However, the navigation methods' performance is
still limited by the scarcity of suitable training data and they suffer from
poor computational scaling.
  In this work, we present PlaceNav, subdividing the robot-independent part
into navigation-specific and generic computer vision components. We utilize
visual place recognition for the subgoal selection of the topological
navigation pipeline. This makes subgoal selection more efficient and enables
leveraging large-scale datasets from non-robotics sources, increasing training
data availability. Bayesian filtering, enabled by place recognition, further
improves navigation performance by increasing the temporal consistency of
subgoals. Our experimental results verify the design and the new method obtains
a 76% higher success rate in indoor and 23% higher in outdoor navigation tasks
with higher computational efficiency.
