Planning Paths Through Unknown Space by Imagining What Lies Therein
Yutao Han, Jacopo Banfi, Mark Campbell

TL;DR
This paper introduces a framework that uses image inpainting on semantically-annotated point clouds to model unknown spaces, improving path planning performance in environments with occlusions.
Contribution
It proposes a novel method combining semantic point clouds and neural inpainting to better predict unknown areas for path planning.
Findings
Enhanced pathfinding performance in unknown environments
Effective use of neural inpainting for semantic point cloud data
Significant improvement over optimistic assumptions in path planning
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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Taxonomy
TopicsRobotics and Sensor-Based Localization · Robotic Path Planning Algorithms · 3D Surveying and Cultural Heritage
