In recent years, there is a growing interest in indoor positioning due to the increasing amount of applications that employ position data. Current approaches determining the location of objects in indoor environments are facing problems with the accuracy of the sensor data used for positioning. A solution to compensate inaccurate and unreliable sensor data is to include further information about the objects to be positioned and about the environment into the positioning algorithm. For this purpose, occupancy grid maps (OGMs) can be used to correct such noisy data by modelling the occupancy probability of objects being at a certain location in a specific environment. In that way, improbable sensor measurements can be corrected. Previous approaches, however, have focussed only on OGM generation for outdoor environments or require manual steps. There remains need for research examining the automatic generation of OGMs from detailed indoor map data. Therefore, our study proposes an algorithm for automated OGM generation using crowd-sourced OpenStreetMap indoor data. Subsequently, we propose an algorithm to improve positioning results by means of the generated OGM data. In our study, we used positioning data from an Ultra-wideband (UWB) system. Our experiments with nine different building map datasets showed that the proposed method provides reliable OGM outputs. Furthermore, taking one of these generated OGMs as an example, we demonstrated that integrating OGMs in the positioning algorithm increases the positioning accuracy. Consequently, the proposed algorithms now enable the integration of environmental information into positioning algorithms to finally increase the accuracy of indoor positioning applications.

中文翻译：

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通过OSM室内数据自动生成的占用网格图改善室内定位

近年来，由于采用位置数据的应用程序数量不断增加，对室内定位的兴趣日益浓厚。当前确定室内环境中物体位置的方法正面临着用于定位的传感器数据准确性的问题。补偿不准确和不可靠的传感器数据的解决方案是在定位算法中包含有关要定位的对象和环境的更多信息。为此，可通过对对象位于特定环境中特定位置的占用概率进行建模，使用占用网格图（OGM）来纠正此类嘈杂数据。这样，可以校正不太可能的传感器测量值。但是，先前的方法仅专注于在室外环境中生成OGM或需要手动操作。仍然需要研究从详细的室内地图数据中自动生成OGM的研究。因此，我们的研究提出了一种使用众包的OpenStreetMap室内数据自动生成OGM的算法。随后，我们提出了一种算法，可通过生成的OGM数据改善定位结果。在我们的研究中，我们使用了来自超宽带（UWB）系统的定位数据。我们对9个不同建筑地图数据集的实验表明，该方法可提供可靠的OGM输出。此外，以这些生成的OGM之一为例，我们证明了将OGM集成到定位算法中可以提高定位精度。所以，