  Recent applications of deep learning to navigation have generated end-to-end
navigation solutions whereby visual sensor input is mapped to control signals
or to motion primitives. The resulting visual navigation strategies work very
well at collision avoidance and have performance that matches traditional
reactive navigation algorithms while operating in real-time. It is accepted
that these solutions cannot provide the same level of performance as a global
planner. However, it is less clear how such end-to-end systems should be
integrated into a full navigation pipeline. We evaluate the typical end-to-end
solution within a full navigation pipeline in order to expose its weaknesses.
Doing so illuminates how to better integrate deep learning methods into the
navigation pipeline. In particular, we show that they are an efficient means to
provide informed samples for sample-based planners. Controlled simulations with
comparison against traditional planners show that the number of samples can be
reduced by an order of magnitude while preserving navigation performance.
Implementation on a mobile robot matches the simulated performance outcomes.
