  Autonomous navigation based on precise localization has been widely developed
in both academic research and practical applications. The high demand for
localization accuracy has been essential for safe robot planing and navigation
while it makes the current geometric solutions less robust to environmental
changes. Recent research on end-to-end methods handle raw sensory data with
forms of navigation instructions and directly output the command for robot
control. However, the lack of intermediate semantics makes the system more
rigid and unstable for practical use. To explore these issues, this paper
proposes an innovate navigation framework based on the GPS-level localization,
which takes the raw perception data with publicly accessible navigation maps to
produce an intermediate navigation cost map that allows subsequent flexible
motion planning. A deterministic conditional adversarial network is adopted in
our method to generate visual goal-directed paths under diverse navigation
conditions. The adversarial loss avoids the pixel-level annotation and enables
a weakly supervised training strategy to implicitly learn both of the traffic
semantics in image perceptions and the planning intentions in navigation
instructions. The navigation cost map is then rendered from the goal-directed
path and the concurrently collected laser data, indicating the way towards the
destination. Comprehensive experiments have been conducted with a real vehicle
running in our campus and the results have verified the robustness to
localization error of the proposed navigation system.
