论文标题
通过想象其中的内容，规划未知空间的路径
Planning Paths Through Unknown Space by Imagining What Lies Therein
论文作者
论文摘要
本文提出了一个新型的框架，用于计划中包含未知空间的地图，例如遮挡。我们的方法将其作为输入语义上注重的点云，并利用图像为介绍的神经网络生成一个合理的未知空间模型，即自由或占用。我们的验证活动表明，有可能大大提高标准探路算法的性能，这些算法采用了将未知空间视为免费的一般乐观假设。
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.
