Planning Paths Through Unknown Space by Imagining What Lies Therein
This addresses path planning in robotics or navigation where unknown spaces are common, but it appears incremental as it builds on existing pathfinding methods with a new modeling technique.
The paper tackles the problem of planning paths in maps with unknown spaces, such as occlusions, by using an image inpainting neural network to model unknown areas as free or occupied, resulting in greatly increased performance for standard pathfinding algorithms.
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.