Ubiquitous WLAN/Camera Positioning using Inverse Intensity Chromaticity Space-based Feature Detection and Matching: A Preliminary Result
This addresses indoor positioning for users in environments like hallways, but it is incremental as it builds on existing WLAN and camera sensor fusion methods.
The paper tackles indoor localization by combining WLAN signal strength with camera-based feature detection using a novel intensity chromaticity space algorithm, achieving preliminary results in an indoor setup without requiring conventional searching algorithms to reduce computational complexity.
This paper present our new intensity chromaticity space-based feature detection and matching algorithm. This approach utilizes hybridization of wireless local area network and camera internal sensor which to receive signal strength from a access point and the same time retrieve interest point information from hallways. This information is combined by model fitting approach in order to find the absolute of user target position. No conventional searching algorithm is required, thus it is expected reducing the computational complexity. Finally we present pre-experimental results to illustrate the performance of the localization system for an indoor environment set-up.