InFo: Indoor localization using Fusion of Visual Information from Static and Dynamic Cameras
Localization in an indoor or Global Positioning System (GPS) denied environment is paramount. It drives various applications that require locating humans or robots in an unknown environment. Various localization systems using different ubiquitous sensors such as camera, radio frequency, inertial measurement unit have been developed. Most of these systems cannot accommodate for scenarios which have substantial changes in the environment such as a large number of people (unpredictable) and sudden change in the environment floor plan (unstructured) In this paper, we propose a system, InFo that can leverage real time visual information captured by surveillance cameras and augment that with images captured by the smart device user to deliver accurate discretized location information. Through our experiments, we demonstrate that our deep learning based InFo system provides an improvement of 10% as compared to a system that does not utilize this real time information.
2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)