ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints PDF
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Description:
Abstract—Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-toend learning problem. However, long-range navigation requires
both planning and reasoning about local traversability, as well
as being able to utilize information about global geography,
in the form of a roadmap, GPS, or other side information,
which provides important navigational hints but may be lowfidelity or unreliable. In this work, we propose a learning-based
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 and attempts to estimate the distance to
the destination for various subgoals. These models are used by a
heuristic planner to decide the best next subgoal 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 imagebased 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 doubling back 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.
both planning and reasoning about local traversability, as well
as being able to utilize information about global geography,
in the form of a roadmap, GPS, or other side information,
which provides important navigational hints but may be lowfidelity or unreliable. In this work, we propose a learning-based
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 and attempts to estimate the distance to
the destination for various subgoals. These models are used by a
heuristic planner to decide the best next subgoal 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 imagebased 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 doubling back 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.
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