A Multi-Floor Indoor Pedestrian Localization Method Using Landmarks Detection for Different Holding Styles

Abstract

The pedestrian dead reckoning (PDR) technique is widely used due to its ease of implementation on portable devices such as smartphones. However, the position error that accumulates over time is the main drawback of this technology. In this paper, we propose a fusion method combining a PDR technique and the landmark recognition methods for multi-floor indoor environments using a smartphone in different holding styles. The proposed method attempts to calibrate the position of a pedestrian by detecting whether the pedestrian passes by specific locations called landmarks. Three kinds of landmarks are defined, which are the WiFi, the turning, and the stairs landmarks, and the detection methods for each landmark are proposed. Besides, an adaptive floor detection method using a barometer and a WiFi fingerprinting technique is suggested for tracking a pedestrian in a multi-floor building. The developed system can track the pedestrian holding a smartphone in four styles. The results of the experiment conducted by three subjects changing the holding style in a three-floor building show the superior performance of the proposed method. It reduces the error rate of positioning results to less than 57.51% compared with the improved PDR alone system.

A Survey of Indoor Localization Systems in Multi-Floor Environments

“…On the other hand, the difference between the barometer measurements can be used to calculate the relative change in height reliably. Therefore, if the height difference between the floors and the initial floor of the user are known, the barometer can be used to detect floor changes and determine the user's floor [180]. Due to the need for an initial floor and to re-calibrate the barometer as time passes, it is often used alongside another technology that can be used in determining the absolute floor, commonly WiFi such as [180]- [182].…”

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

“…Reference [ 55 ] used WiFi RSS and barometer measurements in the update phase of a Kalman filter to estimate the height. References [ 56 , 57 ] used WiFi RSS for floor detection and a barometer to detect stairs. Reference [ 56 ] used a probabilistic model to detect if a recent pressure change was caused by a floor change.…”

“…Therefore, a floor number change was only detected when the change was (almost) finished. Reference [ 57 ] used a moving average to detect floor transitions, but the floor number was detected with a pressure look-up table, which needed frequent recalibration using WiFi fingerprinting. Reference [ 23 ] used WiFi and Bluetooth fingerprinting and detected floor transitions with the barometer and gyroscope.…”

A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting

“…The first group includes the IPSs that do not rely on the infrastructure of a building to determine the user’s position. Some examples for this group are dead reckoning [ 2 ] and image-based [ 3 ] technologies. On the other hand, the IPSs that relate to the building where they are operated belong to the second group.…”

“…Moreover, the high popularization of smart devices such as smartphones or tablets and the compatible implementation of these devices with WiFi systems also leads to the large deployment of this technology for IPS. To enhance the performance of the WiFi-based positioning system, there are hybrid systems that combine the result of the WiFi with other technologies, such as pedestrian dead reckoning [ 2 , 9 ], camera [ 14 ], or magnetic field [ 15 ].…”