Describing indoor maps requires a tremendous amount of data, considering the fact that recently, only the building footprints in OSM surpassed the amount of data of streets.
Additionally, there is not a well agreed-upon model for describing indoor areas. Filtering outliers, extraction of topological information from spatial information and enhancement of existing models with semantic information, are technologies under research. Additionally, since the process of mapping is often crowdsourced, there is a need for mechanisms that manage the heterogeneity of various sources and can bind different inputs for the same floor plans. Furthermore, indoor localization cannot use the maps without them being semantically enhanced with uniquely identified locations. Finally, modeling the accuracy of indoor localization method (provide the correct position), availability (provide results within a constrained time limit), stability (provide consistent results) and ambiguity (provide uncertainty of the results) remain open challenges. Last but not least, there is not an explicitly defined taxonomy of indoor environments.
Dipl.-Ing. Georgios Pipelidis Co-founder and Managing Director of AriadneMaps GmbH