Indoor localization

Overview

 

A huge body of work utilized signal strength of short range signal (such as WiFi, ZeeBee, ultra sound or Infrared) to build a radio map for indoor localization, by deploying a great number of beacon nodes in the building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system is costly and labor-intensive. To overcome that, some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization. The PDR system does not require a beacon-based infrastructure, in which a small number of sensors are put on the pedestrian. These sensors (such as G-sensor and Gyro) are used to estimate the distance and direction that the user traveled. The PDR approach can be generally categorized into two types: foot-mounted and waist-mounted. In general, the foot-mounted system can get accurate step length, but perform poorly in estimated heading direction. On the other hand, the waist-mounted system can estimate direction with high accuracy, but is hard to measure the step length.

In this project, we propose a waist-mounted based PDR to estimate step length using one 3-axis accelerometer. We utilize vertical acceleration to implement double integral for measuring the user’s instant height change and use some physical features of vertical acceleration during the walking to calibrate the measurement. Based on the Pythagorean Theorem, we can then estimate each step length based on the user’s height change during his/her walking. Furthermore, to manage the sensor drift problem which is commonly seen in a PDR system, we propose a novel calibration algorithm using build map information. Unlike the prior work which required a detailed map with precise dimension information, our method can use a simple scale-less floor map, which is generally available for most of the public buildings, to overcome the sensor drift problem. We plan to implement our PDR system on a daily device like PDA or smart phone. 

 

[Publications]

Kun-chan Lan, and Wen-Yuah Shih, "An Intelligent Driver Location System for Smart Parking" Journal of Expert Systems With Applications, Available online 10 October 2013. This article was also featured in "New Scientist" and "Futura-Sciences" magazine. (SCI, IF=1.85)

Kun-Chan Lan, and Wen-Yuah Shih, "On Calibrating the Sensor Errors of a PDR-Based Indoor Localization System," Sensors 2013, no. 4: 4781-4810. (SCI, IF=1.74)