Enabling Topological Planning with Monocular Vision
Name
stein_bradley_preston_icra20.pdf
Description
Accepted version
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
9c5d10f5f0feb36221f9f60dee9c5fd4
Author(s)
Stein, Gregory Joseph
Bradley, Christopher
Preston, Victoria
Roy, Nicholas
Date Issued
September 2020
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
IEEE
Citation
Stein, Gregory Joseph, Bradley, Christopher, Preston, Victoria and Roy, Nicholas. 2020. "Enabling Topological Planning with Monocular Vision." Proceedings - IEEE International Conference on Robotics and Automation.
Version
Author's final manuscript
Abstract
© 2020 IEEE. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA40945.2020.9197484
