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This contribution describes the authors' approach to incremental navigation map generation and enhancement based on Global Positioning System (GPS) track data gathered through tracing the paths of entities with heterogeneous mobility models (e.g., pedestrians, bicyclists, cars, etc.). The authors' generic prototypical implementation is able to generate navigable maps from scratch as well as to enhance existing maps by deriving changes in the original map from the GPS tracks input, and to offer map updates for public download. For the core functionality of incremental semi-automatic map learning, a proprietary graph matching algorithm is combined with a set of models that take the varieties of input data into consideration.
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