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Distributed and Self-organizing Systems
Distributed and Self-organizing Systems


Development and Evaluation of a mobile indoor-mapping editor with speech recognition
Development and Evaluation of a mobile indoor-mapping editor with speech recognition



Research Area

Web Engineering







Openstreetmap is an open source collaborative platform that enables contributors to map places all over the world. For this, key-value pairs are used to tag properties on structures or objects in the environment. Indoor mapping is a subset in this process and has the advantage that architectural maps can often be used as the basis to add tags to geographical entities. As these maps only seldomly include all relevant data for visitors in the buildings, it is often necessary to add more detailed data to the maps manually. In this process, the environment often needs to be visited as visual or contextual information may be important, like accessibility information, height of furniture or its material. Afterwards or on-site with a mobile application, this information has to be added according to the tagging system via an editor, which takes a lot of time and effort.

This thesis should research on an approach that aids the user in adding details to already geographically mapped environments which offers a more intuitive and quicker option to map indoor spaces. While mobile map editors are already existing, the option to use a voice-based user interface is not a main feature in these applications. By reducing the general effort in mapping and offering a more accessible input modality, such a map editor allows a bigger group of volunteers to map spaces. The goal of this thesis is to not enable speech input that substitutes a manual input, but rather offering a modality that uses natural language processing to identify key=value pairs to add information on objects in a map.

The objective of this thesis is the creation of a solution or the combination of existing approaches to solve the problem of an intuitive speech based indoor mapping editor as described above. This comprises the analysis of the state of the art of existing mapping editors, speech recognition methods and natural language processing as well as the demonstration of the solution by prototypical implementation and a suitable evaluation based on the user experience of this prototype and the compliance with requirements that were extracted through the literature research.

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