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.























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ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints PDF

13 Pages·10.3381 MB·other
by  Dhruv Shah, Sergey Levine
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About ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints

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<br> both planning and reasoning about local traversability, as well<br> as being able to utilize information about global geography,<br> in the form of a roadmap, GPS, or other side information,<br> which provides important navigational hints but may be lowfidelity or unreliable. In this work, we propose a learning-based<br> approach that integrates learning and planning, and can utilize<br> side information such as schematic roadmaps, satellite maps<br> and GPS coordinates as a planning heuristic, without relying<br> on them being accurate. Our method, ViKiNG, incorporates a<br> local traversability model, which looks at the robot’s current<br> camera observation and a potential subgoal to infer how easily<br> that subgoal can be reached, as well as a heuristic model, which<br> looks at overhead maps and attempts to estimate the distance to<br> the destination for various subgoals. These models are used by a<br> heuristic planner to decide the best next subgoal in order to reach<br> the final destination. Our method performs no explicit geometric<br> reconstruction, utilizing only a topological representation of the<br> environment. Despite having never seen trajectories longer than<br> 80 meters in its training dataset, ViKiNG can leverage its imagebased learned controller and goal-directed heuristic to navigate to<br> goals up to 3 kilometers away in previously unseen environments,<br> and exhibit complex behaviors such as probing potential paths<br> and doubling back when they are found to be non-viable. ViKiNG<br> is also robust to unreliable maps and GPS, since the low-level<br> controller ultimately makes decisions based on egocentric image<br> observations, using maps only as planning heuristics.</br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br>

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Author:Dhruv Shah; Sergey Levine
Pages:13
Language:other
File Size:10.3381
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