Nowadays, the importance and utilization of spatial information are recognized. Particularly in urban areas, the demand for indoor spatial information draws attention and most commonly requires high-precision 3D data. However accurate, most methodologies present problems in construction cost and ease of updating. Images are accessible and are useful to express indoor space, but pixel data cannot be applied directly to provide indoor services. A network-based topological data gives information about the spatial relationships of the spaces depicted by the image, as well as enables recognition of these spaces and the objects contained within. In this paper, we present a data fusion methodology between image data and a network-based topological data, without the need for data conversion, use of a reference data, or a separate data model. Using the concept of a Spatial Extended Point (SEP), we implement this methodology to establish a correspondence between omnidirectional images and IndoorGML data to provide an indoor spatial service. The proposed algorithm used position information identified by a user in the image to define a 3D region to be used to distinguish correspondence with the IndoorGML and indoor POI data. We experiment with a corridor-type indoor space and construct an indoor navigation platform.

中文翻译：

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通过空间数据融合将图像和基于网络的拓扑数据集成到室内基于位置的服务中

如今，人们认识到空间信息的重要性和利用。特别是在城市地区，对室内空间信息的需求引起了人们的注意，并且最常见的是需要高精度3D数据。无论哪种方法准确无误，大多数方法都存在建筑成本和易于更新的问题。图像是可访问的，并且对于表示室内空间很有用，但是像素数据无法直接应用于提供室内服务。基于网络的拓扑数据可提供有关图像所描绘的空间的空间关系的信息，并能够识别这些空间以及其中包含的对象。在本文中，我们提出了一种在图像数据和基于网络的拓扑数据之间的数据融合方法，而无需进行数据转换，使用参考数据或单独的数据模型。使用空间扩展点（SEP）的概念，我们实现了此方法，以在全向图像和IndoorGML数据之间建立对应关系，以提供室内空间服务。所提出的算法使用了用户在图像中标识的位置信息来定义3D区域，该区域将用于区分与IndoorGML和室内POI数据的对应关系。我们尝试使用走廊型的室内空间，并构建一个室内导航平台。