论文标题
地板：机器人技术平面图的生成矢量图形模型
FloorGenT: Generative Vector Graphic Model of Floor Plans for Robotics
论文作者
论文摘要
平面图是在室内环境中进行推理和沟通的基础。在本文中，我们表明，通过将平面平面图作为线段序列进行建模，从特定的角度来看，自动回归序列建模的最新进展可以利用以建模并预测平面图。线段被规范化并转换为令牌序列，基于注意力的神经网络用于拟合近代令牌的一步分布。我们将网络适合于从一组大型平面图中得出的序列，并在四种情况下演示了该模型的功能：新颖的平面图生成，完成部分观察到的平面图，从模拟传感器数据中产生平面图的生成，最后，落地平面图与环境部分知识的最短距离预测平面图的适用性。
Floor plans are the basis of reasoning in and communicating about indoor environments. In this paper, we show that by modelling floor plans as sequences of line segments seen from a particular point of view, recent advances in autoregressive sequence modelling can be leveraged to model and predict floor plans. The line segments are canonicalized and translated to sequence of tokens and an attention-based neural network is used to fit a one-step distribution over next tokens. We fit the network to sequences derived from a set of large-scale floor plans, and demonstrate the capabilities of the model in four scenarios: novel floor plan generation, completion of partially observed floor plans, generation of floor plans from simulated sensor data, and finally, the applicability of a floor plan model in predicting the shortest distance with partial knowledge of the environment.
