Evaluating the Impact of Hybridization of Vision and Sensor-Based Tracking on the Accuracy and Robustness of Virtual Reality-Based Shooting Tutor for Defense Training

doi:10.23940/ijpe.23.09.p1.559567

Abstract

Abstract: Virtual Reality (VR) is an immersive simulation of the environment. The quality of tracking in VR applications is measured in terms of accuracy and robustness. Tracking based on sensing the environment using only one type of sensor may not be sufficient to meet the requirements of VR applications. Hybrid tracking based on inputs from two or more sensors can cope with the challenging needs of such applications. This paper discusses the implementation of two mobile-based VR applications designed for Military shooting training. The first application is developed using a single type of tracking technique named Virtual Shooting Tutor (VST) without hybrid tracking. In the second application, the inputs from external Micro Electro-Mechanical Systems (MEMS) gyroscope and those from mobile integrated gyroscope are hybridized. The application is named VST with hybrid tracking. Two versions of VST are compared with each other to evaluate the impact of hybrid tracking on the accuracy and robustness of the VR application.

Key words: virtual reality, mems sensors, accelerometer, gyroscope, hybrid tracking, accuracy, robustness, military training

Amanpreet Kaur and Archana Mantri. Evaluating the Impact of Hybridization of Vision and Sensor-Based Tracking on the Accuracy and Robustness of Virtual Reality-Based Shooting Tutor for Defense Training [J]. Int J Performability Eng, 2023, 19(9): 559-567.

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