2 research outputs found
Enabling Topological Planning with Monocular Vision
Topological strategies for navigation meaningfully reduce the space of
possible actions available to a robot, allowing use of heuristic priors or
learning to enable computationally efficient, intelligent planning. The
challenges in estimating structure with monocular SLAM in low texture or highly
cluttered environments have precluded its use for topological planning in the
past. We propose a robust sparse map representation that can be built with
monocular vision and overcomes these shortcomings. Using a learned sensor, we
estimate high-level structure of an environment from streaming images by
detecting sparse vertices (e.g., boundaries of walls) and reasoning about the
structure between them. We also estimate the known free space in our map, a
necessary feature for planning through previously unknown environments. We show
that our mapping technique can be used on real data and is sufficient for
planning and exploration in simulated multi-agent search and learned subgoal
planning applications.Comment: 7 pages (6 for content + 1 for references), 5 figures. Accepted to
the 2020 IEEE International Conference on Robotics and Automatio