# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 1 Introduction  
Indoor maps have become a necessity in robotics, augmented reality, location-based services, mobile ad hoc networks, and search and rescue missions. Because of the high manual effort of generating indoor maps, there have emerged approaches for the dynamic generation of two-dimensional indoor maps through crowdsourced sensor data (e.g., [1,2]). However, these approaches require precise localization. Although many localization providers argue having achieved an average accuracy of 6 m in horizontal localization, none of them provides vertical localization. This has as a result pushed back milestones scheduled by initiatives that are focused on accelerating the research of indoor localization, as these milestones require storey-level localization. Such initiatives are the Enhanced 911 [3] in the United States, and the Enhanced 112 in the European Union [4], as well as the European Accessibility Act [5]. The main reason for the lack of vertical localization providers is the limited information available, for example, the lack of precise altitude indication for every floor in a building in existing maps. To the best of our knowledge, no approach for the dynamic vertical mapping using crowdsourced smartphone sensor data has been proposed. This paper aims to automate the indoor vertical mapping process, while enriching existing maps with indoor information. In this way, we enable maps to carry information regarding the number of floors in a building and the corresponding altitude of each floor. We achieve this using the novel method we use to fuse the barometric sensor of smartphones with other sensors for the extraction of the ambient reference pressure in locations, which can be used for precise altitude estimation. More specifically, we first use sensor data extracted from light, proximity, Global Positioning System (GPS), and magnetic sensors to identify the user's transition from the outside to the inside of a building. Once we recognize this transition, we use it as a landmark for the extraction of the reference pressure. We then use this extracted reference pressure to estimate the altitude differences for every step of the user using the barometric formula. For better clustering between altitude values, we filter out vertical transitions (e.g., stairs or elevators), as they do not belong to floors. Because there is no user who is going to visit all the floors of a building, altitude values from multiple users are aggregated for the identification of the number of floors in a building and the height of each floor. Finally, these data are used to generate three-dimensional (3D) models following the standards as defined by the City Geography Markup Language (CityGML) Level of Detail 2, while an enhancement to the standard models is proposed in order to enable it to carry floor information as well as the altitude of each floor. Various studies attempt to vertically localize humans or objects via pressure sensors ([PERSON], 2016; [PERSON], 2016). However, they all assume reference sensor stations permanently installed in the building. Hence, these are highly infrastructure-dependent approaches. Additionally, several studies attempt to vertically localize objects or humans, mostly triangulating them, using the WiFi received signal strength ([PERSON], 2016), cellular network antennas ([PERSON], 2016) or Bluetooth Low Energy (BLE) beacons ([PERSON], 2016). Unfortunately, every triangulation method highly depends on the assumption of the existence of particular infrastructure, as well as the line of sight. This means that the strength of the signal, and as a consequence the distance estimation, is influenced when the observer is standing in front of the infrastructure (e.g., BLE beacons) or behind it. Finally, approaches for the dynamic generation of vertical maps have also been proposed ([PERSON], 2016; [PERSON], 2016; [PERSON], 2016). However, these approaches suggest the use of outdoor characteristics for mapping indoors.