Context Rich Hybrid Navigation Using WiFi and Geomagnetic Sensors in Smartphones and Map Generation Using Lidar

Abstract

Navigation systems perform a huge role in traveling component of life. Most importantly it helps people to get to places even in foreign or unfamiliar environments. This research introduces a way of mapping environments with less effort, which shows that mapping an indoor environment is an easy task that could be performed by any tech savvy individual. This has been done by examining several projects and researches conducted by various personnel and organizations including NASA. It has become clear that the technology `LIDAR,' is clearly feasible for the requirement of indoor map generation. A software was later built to accommodate the device which is built using LIDAR and to give the user a better experience in map generation. The software helps to overcome the limitations that are imposed by the device. The overall product with the device and software integrated provides an ideal low-budget solution for the users. The proposed system service features three highly desirable properties, namely accuracy, scalability, and crowdsourcing. IPS is implemented with a set of crowdsourcing-supportive mechanisms to handle the collective amount of raw data, filter incorrect user contributions and exploit Wi-Fi data from diverse mobile devices. Furthermore, it uses a big-data architecture for efficient storage and retrieval of localization and mapping data. In this research, the service relies on the sensitive data collected by smartphones (Wi-Fi signal strength and geomagnetic measurements) to deliver reliable indoor geolocation information.