Abstract
Abstract. With every new generation of smart devices, new sensors are introduced, such as depth camera or UWB sensors. Combined with the rapidly growing number of smart mobile devices, indoor positioning systems (IPS) have seen increasing interest due to numerous indoor location-based services (ILBS) and mobile applications at large. Wi-Fi Received Signal Strength (RSS) based fingerprinting positioning (WF) techniques are popularly used in many IPS as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks, as this technique requires no line-of-sight to the access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices. However, WF techniques have problems with fingerprint variance, i.e., fluctuation of the sensed signal, and efficient map updating due to the frequently changing environment. To address these problems, we propose a novel framework of IPS which uses particle filter to fuse WF and state-of-the-art CNN-based visual localization method to better adapt to changing indoor environment. The suggested system was tested with real-world crowdsourced data collected by multiple devices in an office hallway. The experimental results demonstrate that the system can achieve robust localization at a 0.3~1.5 m mean error (ME) accuracy, and map updating with a 79% correction rate.
Highlights
Location estimation is the essential procedure for several Indoor Location Based Services (ILBS) such as rescue management, patient monitoring in hospitals, and security applications that require a meter-level accuracy
We propose a novel system with particle filter (PF)based sensor fusion technology which integrates positioning estimates calculated with Wi-Fi received signal strength (RSS) and visual data to simultaneously achieve efficient indoor positioning and radio map updating
A novel particle filter-based sensor fusion indoor positioning system that works with Wi-Fi and camera data from smart devices is presented in this chapter
Summary
Location estimation is the essential procedure for several Indoor Location Based Services (ILBS) such as rescue management, patient monitoring in hospitals, and security applications that require a meter-level accuracy. The facing direction estimation is needed for the navigation applications which guide users from point A to point B no matter indoor or outdoor. Along with the proliferation of using smartphones, the ILBS solutions specified for the sensors embedded in smartphones have been gaining attention due to the increasingly emerging indoor commercial application market. Typical requirements for these applications are userfriendly, which means ease of use, low cost, robustness, high accuracy, easy to deploy, easy of calibration, and universal availability. Since GPS does not work indoors, many alternative localization techniques, based on various smartphone-equipped sensors/signals have been proposed to estimate user location. Wi-Fi received signal strength (RSS) based methods attracted continuous attention, as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks; the technique does not need line-of-sight to access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices
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