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Planning Paths Through Unknown Space by Imagining What Lies Therein
November 14, 2020 Β· Declared Dead Β· π Conference on Robot Learning
"No code URL or promise found in abstract"
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Authors
Yutao Han, Jacopo Banfi, Mark Campbell
arXiv ID
2011.07316
Category
cs.RO: Robotics
Citations
15
Venue
Conference on Robot Learning
Last Checked
4 months ago
Abstract
This paper presents a novel framework for planning paths in maps containing unknown spaces, such as from occlusions. Our approach takes as input a semantically-annotated point cloud, and leverages an image inpainting neural network to generate a reasonable model of unknown space as free or occupied. Our validation campaign shows that it is possible to greatly increase the performance of standard pathfinding algorithms which adopt the general optimistic assumption of treating unknown space as free.
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