# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 2 Related Work  
Enhancing CityGML models with indoor geometry has already been discussed in [11]. In this study, the LoD2+ method was introduced. The method is robust and was implemented successfully using Nef Polyhedra. However, the authors used some prior knowledge, such as building facades and available data modeled following the LoD2 format. As a result, this method is not applicable to general cases because not all buildings contain sufficient information that can be used for mapping indoor areas. Apple holds a patent that focuses on the visualization of information in indoor 3D places [16]. They do not consider altitude estimation, but instead they assume the existence of indoor maps with locations that specify where vertical transitions may occur, annotated on the map, and a two-dimensional localization mechanism. Additionally, they assume that users can be localized in a particular floor using a particle filter-based framework, which is responsible for assessing the probability of a vertical transition. In this framework, the confidence is quantified on the basis of WiFi access points and the receive signal strength. [PERSON] et al. [17] point out the need of detecting vertical transitions because of the limitation of the Zero Velocity Update (ZUPT) algorithm to identify vertical displacements. To solve this problem, they introduce a moving platform detection module. This works by combining accurate sensors, not those available on a smartphone, such as an accelerometer, barometer and magnetometer. These use ZUPT for localization and a Simultaneous Localization and Mapping (SLAM) algorithm for reducing the remaining drift. They estimate altitude using the barometric sensor, while they also use it to identify landmark phases. In addition, they attempt to identify the boundaries of vertical movements. The intuition for the use of acceleration for the detection of vertical transitions is that the acceleration caused by external factors is weaker than that caused by the pedestrian. However, their approach focuses on correcting real-time localization and assumes the existence of indoor maps. [PERSON] et al. [7] suggest using barometers for 2.5-dimensional (2.5D) (floor-level) localization. They examine how the barometric formula performs for altitude determination. They researched the robustness of altitude estimation on different devices that record differences from 2.1 to 2.5 hPa, which is translated to an offset of multiple floors. They noted that the variation of pressure over 2 h could reach an equivalent of a 10 m height change. They also examined latency robustness as well as stability in the short term, where they noticed changes of 0.1 hPa every 10 min. On the basis of their experiments, they argue that it is impossible to accurately determine height using a barometer in an indoor environment in an absolute manner. They strongly point out the necessity of a reference station. In their study, they used a reference station 5 km away. However, a reference station is not always available, and using other devices such as reference stations requires calibration, which is not realistic in a real-world scenario. [PERSON] et al. [6] propose the use of multiple barometers as reference points for the floor positioning of smartphones with built-in barometric sensors. This method does not require knowledge of the accurate heights of buildings and storeys. It is robust against temperature and humidity, and it considers the difference in the barometric pressure-change trends and different floors. The intuition is that atmospheric pressure decreases as the altitude increases. Hence, pressure changes that correspond to altitude changes are possible to be calculated using a reference pressure and the barometric formula. As they argue, humidity does not significantly affect the accuracy of the system for indoor altitude estimation; thus, they use the gas constant for dry air and the air molar mass of dry air instead of humid air.