  Robotic navigation has been approached as a problem of 3D reconstruction and
planning, as well as an end-to-end learning problem. However, long-range
navigation requires both planning and reasoning about local traversability, as
well as being able to utilize general knowledge about global geography, in the
form of a roadmap, GPS, or other side information providing important cues. In
this work, we propose an approach that integrates learning and planning, and
can utilize side information such as schematic roadmaps, satellite maps and GPS
coordinates as a planning heuristic, without relying on them being accurate.
Our method, ViKiNG, incorporates a local traversability model, which looks at
the robot's current camera observation and a potential subgoal to infer how
easily that subgoal can be reached, as well as a heuristic model, which looks
at overhead maps for hints and attempts to evaluate the appropriateness of
these subgoals in order to reach the goal. These models are used by a heuristic
planner to identify the best waypoint in order to reach the final destination.
Our method performs no explicit geometric reconstruction, utilizing only a
topological representation of the environment. Despite having never seen
trajectories longer than 80 meters in its training dataset, ViKiNG can leverage
its image-based learned controller and goal-directed heuristic to navigate to
goals up to 3 kilometers away in previously unseen environments, and exhibit
complex behaviors such as probing potential paths and backtracking when they
are found to be non-viable. ViKiNG is also robust to unreliable maps and GPS,
since the low-level controller ultimately makes decisions based on egocentric
image observations, using maps only as planning heuristics. For videos of our
experiments, please check out our project page
https://sites.google.com/view/viking-release.
