Grammar-based reconstruction of building interiors by mobile participants

地图是基于位置的服务的必要先决条件。虽然街道地图甚至详细的3D城市模型今天已经可用，但由于创建详细的室内模型的巨大手动工作，室内模型仍然在很大程度上缺失。用于室内建模的协作方法试图通过从传感器数据（如由一群移动的用户收集的运动轨迹和相机图像）自动导出室内模型来解决这个问题。因此，第二个资助期的主要目标是设计从协作收集的传感器数据自动创建3D室内模型的方法。起点是在第一个资助期内设计的方法，用于从移动的设备群收集的运动轨迹创建2D室内模型。第二个重要的基础是在第一个资助期内设计的室内语法概念，它描述了2D平面图的结构规则。在第一个资助期间，我们可以证明室内语法非常有利于提高模型质量以及参与收集传感器数据的移动的设备的能源效率。因此，第二个资助期也侧重于基于语法的方法，现在是为了促进三维室内模型的生成。从2D平面图的基于语法的建模到包括不同高度的走廊和房间结构以及包含在房间中的内部对象的固有的更复杂的3D模型的过渡需要用于生成这样的3D室内模型的增强的语法概念和方法。第二个任务是语义信息的语法扩展和利用这些信息的模型生成。与第一个供资期类似，能效仍然是第二个供资期的一个重要目标。除了移动的设备对传感器数据的能量高效收集之外，对所收集的数据的能量高效处理成为一个巨大的挑战。这就提出了模型生成功能在移动的设备和服务器基础设施之间的最佳分布的问题。与此密切相关的是调查分布式收集和处理传感器数据所需的信息的节能通信。

Maps are an essential prerequisite for location-based services. Although street maps and even detailed 3D city models are already available today, indoor models are still largely missing due to the great manual effort of creating detailed indoor models. Collaborative approaches for indoor modelling try to solve this problem by deriving indoor models automatically from sensor data like movement trajectories and camera images gathered by a crowd of mobile users. Therefore, the major goal of the second funding period is to design methods for the automatic creation of 3D indoor models from collaboratively gathered sensor data. Starting point are the methods designed during the first funding period for the creation of 2D indoor models from movement trajectories gathered by crowds of mobile devices. A second important foundation is the concept of indoor grammars designed in the first funding period, which describe structural rules of 2D floor plans. During the first funding period, we could show that indoor grammars are highly beneficial to improve model quality as well as energy efficiency of mobile devices participating in collecting sensor data. Therefore, also the second funding period focuses on grammar-based methods, now to facilitate the generation of indoor models in three dimensions. The transition from the grammar-based modelling of 2D floor plans to inherently more complex 3D models including corridors and room structures of different heights as well as interior objects contained in rooms requires enhanced grammar concepts and methods for the generation of such 3D indoor models. A second task is the extension of grammars by semantic information and the utilization of this information for model generation. Similarly to the first funding period, energy efficiency remains an important goal also of the second period. Additionally to the energy efficient gathering of sensor data by mobile devices, the energy efficient processing of gathered data becomes a great challenge. This raises the question for an optimal distribution of model generation functions between mobile devices and server infrastructure. Closely related are investigations for the energy efficient communication of information required for the distributed gathering and processing of sensor data.