Title: Positioning Algorithm Adaptation of an Indoor Navigation System for Virtual Reality Game Applications
Author(s): Mengdi Jia, Sihao Zhao, Dengyue Dong, Xiaowei Cui, Mingquan Lu
Published in: Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016)
September 12 - 16, 2016
Oregon Convention Center
Portland, Oregon
Pages: 1824 - 1830
Cite this article: Jia, Mengdi, Zhao, Sihao, Dong, Dengyue, Cui, Xiaowei, Lu, Mingquan, "Positioning Algorithm Adaptation of an Indoor Navigation System for Virtual Reality Game Applications," Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 1824-1830.
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Abstract: Virtual reality (VR) related technologies have been regarded as a future hotspot in various kinds of application areas. In most of the existing VR game systems, players have to stay at a fixed location wearing a head-mounted display (HMD) and interact with the moving virtual scenarios, which may cause the feeling of sickness. VR games that uses a localization system enable the players to move inside a specific area which enhances the sense of immersion. However, due to the imprecise position information that cannot correctly reflect the user movement, players still feel dizzy. In order to solve this problem, we develop an indoor navigation system and propose a localization algorithm for VR game applications to provide accurate and smooth positioning information for virtual environment generation so that players can move in the real world and interact with the virtual scenario that is consistent with their real positions and movements. We implement this system with both ranging devices based on ultra wide band (UWB) technology and inertial measurement unit (IMU). Extended Kalman Filter (EKF) is applied in the positioning algorithm to fuse the range and IMU measurements. Zero velocity update (ZUPT) correction and robust estimation to detect and remove outliers are also used in our algorithm. Experiments in indoor environment prove that the algorithm can provide positioning results accurately and smoothly.