Floor Plan Based Indoor Vision Navigation Using Smart Device
Creators: name: Huang, Bei
Publisher: name: Graduate Studies
Year: 2013
Subjects: subject: Artificial Intelligence
subject: Computer Science
subject: Robotics
subject: indoor positioning
subject: vision navigation
subject: geo-reference database

description: The Global Positioning System (GPS) nowadays is sized down to a chip sensor and built into almost every smart phone and tablet. Therefore, navigation using those intelligent gadgets becomes a must-have function. GPS has been widely employed for outdoor navigation, while its performance suffers from severe degradation in challenging scenarios such as urban canyon and indoor. Due to the overwhelming signal noise, building reflection and blockage, indoor navigation using GPS frequently encounters poor accuracy or even signal outage. In order to improve the service availability and navigation accuracy, inertial measurement units (IMU) are integrated with GPS, which continuously measures the user acceleration and rotation rate. Integrating these relative motion measurements derives the position, velocity and orientation, therefore it bridges the gap during GPS outage. However, IMU raw measurements are contaminated by sensor bias and drift, and for low-cost sensors on smart devices, the bias and drift are extremely severe and unstable. The navigation solution derived from these poor quality sensors results in significant accumulative errors, which will destroy the system reliability very soon. Furthermore, most smart devices embrace cellular and Wi-Fi network positioning to improve service availability, time-to-first-fix, accuracy and reliability in indoor scenarios. Unfortunately, network based positioning performance highly depends on the signal reception, and quality of the database of Wi-Fi access points (APs) and cellular towers. Based on our experiments, network based positioning performance can merely achieve tens of meters position accuracy on average. For indoor navigation application, however, users’ expectation is room-level, turn-by-turn navigation guidance. In this thesis, a vision navigation system is developed for pedestrian indoor navigation using smart device. In order to derive the three-dimensional camera position from the monocular camera vision, a geo-reference database is needed. Floor plan is a ubiquitous geo-reference database that every building refers to it during construction and facility maintenance. Comparing with other popular geo-reference database such as geo-tagged photos, the generation, update and maintenance of floor plan database does not require costly and time consuming survey tasks. In the proposed system, user is asked to take a picture of the surrounding indoor scenario, and a robust feature matching method is designed to match the indoor features contained in the camera image to those in the floor plan database. Given the image-to-floor plan feature correspondences, a navigation algorithm is developed to integrate the monocular vision with the floor plan geo-reference information and derive the camera position and orientation. The vision navigation system is realized on an iOS App and tested with iPad in various indoor scenarios. The test results show that, comparing with Wi-Fi positioning, the proposed system has improved the position accuracy from tens of meters to 5 m on average.