Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

Abstract

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today's devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multisensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.

Toward a unified theoretical framework for photogrammetry

“…As shown in Figure 1, the physics of imaging involves five processes (Bakker et al 2001;Peng, Zhang, and Shan 2015). The lighting process needs one or more light sources like the sun or skylight.…”

“…When dealing with other objects such as buildings, spherical harmonics (Basri and Jacobs 2001) are introduced, which use several variables and the surface normal to represent the imaging process for the scene. Based on the method of (Peng, Zhang, and Shan 2015) in which the 90 m SRTM is refined to 30 m with Landsat 8 imagery, the GaoFen-1 image is used to refine the SRTM from a resolution of 90 m to 15 m. The original resolution of GaoFen-1 image is 16 m and the resolution of SRTM is 90 m. At first, both the image and DEM are resampled to 15 m. Then, the shadingbased DEM refinement algorithm is applied. At last, the DEM is refined to 15 m. As shown in Figure 6, the refined DEM is with higher resolution and with more geospatial details comparing to the original DEM.…”

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

“…In device-based systems, the location is determined from a user-centric perspective by predominantly utilizing the smartphone as the localization device. This enables, e.g., navigation within complex environments [ 7 , 8 , 9 ]. Furthermore, it has the advantage that the user can determine his/her location without the requirement of data exchange with a centralized entity.…”

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

“…In the step detection process, acceleration data is usually used to analyze the number of steps walked, such as peak detection, zero crossing and flat zone detection [14]. In stride length estimation, commonly used methods include linear or non-linear estimation [15]. For heading estimation, researchers usually use accelerometers, magnetometers, and gyroscopes to determine the direction of pedestrians [16].…”