Figure 1: Figure 1: Location Based Services, Robotics, Augmented Reality and Mobile Ad-hoc Networks are strongly connected with the existence of Indoor Maps.
Challenges and Objectives
Figure 2: An example of an enhanced CityGML Level of Detail 2+ (LoD) which carries information about the number of floors and their altitude.
This project aims to introduce a model that will enable the integration of dynamic generated semantic annotated indoor maps. In this way other platform services will be enhanced with semantic geo-information. After the introduction of such a model, the integration of existing indoor maps to service providers will be enabled. Those maps will be following existing standards, such as CityGML and IndoorGML. Finally, the homogeneous B2B integration of indoor maps with semantic descriptions will be enabled (e.g. Figure 2).
Figure 3: An example of a data flow diagram on how a dynamic mapping process could potentially come into reality. The chart presents the entire flow of information as gradual steps beginning from identifying reference locations such as entrances or other uniquely identified locations that belong to the essential outdoor indoor transition. After having successfully obtained a reference location, for localizing a human in the most infrastructure independent way, we need 01 his walking direction and 02 his step length. Having successfully localize a human indoors and 03 examining the properties of the places he has visited, characteristic locations indoors can be recognized. Additionally mapping human traces indoors, 04 a point cloud of the building can be generated. Segmented this point cloud based on WiFi signal reflections on walls 05 rooms of the structure can be recognized and 09 a map that describes the geometry of the indoor place can be emerged. 07 By grouping the human traces based on their time characteristics, 08 the topology of the indoor place can be recognized. 09 Finally, identifying the context of the person in specific locations, a semantic map can be generated.
Current focusWe believe that there will be no single way for mapping indoor places, but rather a diverse set of techniques and services will be used to build up maps and services for indoor locations in a customized way .
Figure 4:  An example of a “take me to the exit” service. ariadne, provides to the user the nearest exit from the subway station to his destination, the nearest compartment to his exit, as well as indoor routing from the subway platform to the exit.
An example of such an intermediate service is the semantic annotation of an indoor place. We hypothesize that user context is place-dependent. Hence, the semantic annotation could be attempted based on user activities. For example, a stair can be identified by identifying the “climbing stairs” activity. User context can be extracted in various ways, using calendar data, location and time. In our research, we opportunistically extract user context via reasoning on activities, through a mobile application (Figure 5). The user activities are recognized from smartphone data, after these data are segmented into clusters based on defined constraints.
Figure 5:  The recordData application. It’s goal is to collect data from user smartphones and stream them on our server. It can recognize the following set activities. Sitting, Standing, Walking, Walking upstairs, Walking downstairs, Using the elevator up and Using the elevator down.
Obtaining data from compass, accelerometer, gyroscope, pedometer and ambient pressure, through the recordData application enables Dead Reckoning. Dead Reckoning is a localization method where the current location is estimated based on the previous location, the direction and the distance traveled.
Figure 6: Segmentation of data for activity recognition. Yellow: Sitting, Magenta: Standing, Blue: Walking, Cyan: Walking Upstairs, Red: Walking Downstairs, Green: Elevator Up, White: Elevator Down
Figure 7: Example of user traces estimated with Dead Reckoning, Activity Recognition and Altitude estimation using the barometric formula. Blue: Walking, Red: Walking upstairs.
Finally, through computational geometry algorithms the geometry, the topology and the semantics of the indoor space will be identified and mapped following existing standards, such as CityGML, IndoorGML and others.
If you would like to get more information about this subproject or if you are interested in a cooperation to develop indoor mapping/localization/navigation solutions, please contact Dr. Christian Prehofer or Georgios Pipelidis. Motivated students who are looking for a thesis or a guided research in one of the presented topics are always invited to contact us as well.
 G. Pipelidis and C. Prehofer. “Models and Tools for Indoor Maps.” Digital Mobility Platforms and Ecosystems (2016): 154.
 M. Alzantot and M. Youssef, ”CrowdInside: Automatic Construction of Indoor Floorplans”, in Proceedings of the 20th International Conference on Advances in Geographic Information Systems, New York, NY, USA, 2012, pp. 99-108.
 D. Philipp et al., ”Mapgenie: Grammar-enhanced indoor map construction from crowd-sourced data”, in Pervasive Computing and Communications (PerCom), 2014 IEEE International Conference on, 2014, pp. 139-147.
 G. Pipelidis, S. Xiang, and C. Prehofer. ”Generation of indoor navigable maps with crowdsourcing”. Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia. ACM, 2016.
 G. Pipelidis, X. Su, C. Prehofer, “Generation of indoor navigable maps with crowdsourcing”, 15th International Conference on Mobile and Ubiquitous Multimedia, Rovaniemi, Finland, Dec 12 – Dec 15, 2016
 G. Pipelidis, C. Prehofer, I. Gerostathopoulos, “Adaptive Bootstrapping for Crowdsourced Indoor Maps”, 3rd International Conference on Geographical Information Systems Theory, Applications and Management, Porto, Portugal, 27th to 28th April 2017
 G. Pipelidis, O. R. Moslehi Rad, D. Iwaszczuk, C. Prehofer, U. Hudentobler, “A Novel Approach for Dynamic Vertical Indoor Mapping through Crowd-sourced Smartphone Sensor Data”, 8th International Conference on Indoor Position Indoor Navigation, 18-09-2017, Sapporo, Japan.