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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References

1. Caserman P.,Garcia-Agundez, A., Konrad, R., Göbel, S., and Steinmetz, R. Real-Time Body Tracking in Virtual Reality using a Vive Tracker. Virtual Reality, vol. 23, pp. 155-168, 2019.
2. Goldfeather J.Tracking in Virtual Reality. Math Horizons, vol. 10, no. 3, pp. 27-31, 2003.
3. Malhotra S., Kumar A., andDutta R.Effect of Integrating IoT Courses At the Freshman Level on Learning Attitude and Behaviour in the Classroom. Education and Information Technologies, vol. 26, pp. 2607-2621, 2021.
4. Zhou, F., Duh, H.B.L., and Billinghurst, M. Trends in Augmented Reality Tracking, Interaction and Display: A Review of Ten Years of ISMAR. In2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, IEEE, pp. 193-202, 2008.
5. Windolf M., Götzen N., andMorlock M.Systematic Accuracy and Precision Analysis of Video Motion Capturing Systems—Exemplified on the Vicon-460 System. Journal of biomechanics, vol. 41, no. 12, pp. 2776-2780, 2008.
6. Fernandez, J.C., Mounier, L., and Pachon, C. A Model-Based Approach for Robustness Testing. In Testing of Communicating Systems: 17th IFIP TC6/WG 6.1 International Conference, TestCom 2005, Montreal, Canada, May 31-June, 2005. Proceedings 17, Springer Berlin Heidelberg, pp. 333-348, 2005.
7. Trivedi N.K., Anand A., Sagar P., Batra N., Noonia A., andKumar A.A Systematic Review of Tools Available in the Field of Augmented Reality. Journal of Cases on Information Technology (JCIT), vol. 24, no. 4, pp. 1-9, 2022.
8. You, S. and Neumann, U.Fusion of Vision and Gyro Tracking for Robust Augmented Reality Registration. InProceedings IEEE Virtual Reality 2001, IEEE, pp. 71-78, 2001.
9. Ho N., Wong P.M., Chua M., andChui C.K.Virtual Reality Training for Assembly of Hybrid Medical Devices. Multimedia Tools and Applications, vol. 77, pp. 30651-30682, 2018.
10. Yang X., Guo J., Xue T., andCheng K.T.Robust and Real-Time Pose Tracking for Augmented Reality on Mobile Devices. Multimedia Tools and Applications, vol. 77, pp. 6607-6628, 2018.
11. Piekarski, W., Avery, B., Thomas, B.H., and Malbezin, P. Hybrid Indoor and Outdoor Tracking for Mobile 3D Mixed Reality. In The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings, IEEE Computer Society, pp. 266-266, 2003.
12. Rabbi I., Ullah S., andKhan S.U.Augmented Reality Tracking Techniques—A Systematic Literature. IOSR Journal of Computer Engineering (IOSRJCE), vol. 2, no. 2, pp. 23-29, 2012.
13. Liang H., Ge C., Liang F., andSun Y.VR-Based Training Model for Enhancing Fire Evacuee Safety. International Journal of Performability Engineering, vol. 16, no. 1, pp. 107, 2020.
14. Fast K., Gifford T., andYancey R.Virtual Training for Welding. Inthird IEEE and ACM international symposium on mixed and augmented reality, IEEE, pp. 298-299, 2004.
15. Lin F., Ye L., Duffy V.G., andSu C.J.Developing Virtual Environments for Industrial Training. Information sciences, vol.140, no. 1-2, pp. 153-170, 2002.
16. Julier S., Baillot Y., Lanzagorta M., Brown D., andRosenblum L.Bars: Battlefield Augmented Reality System. In NATO Symposium on Information Processing Techniques for Military Systems, Istanbul: IEEE Computer Society Press, pp. 9-11, 2000.
17. Miyato T., Maeda S.I., Koyama M., andIshii S.Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 8, pp. 1979-1993, 2018.
18. Capps M.,McDowell, P., and Zyda, M. A Future for Entertainment-Defense Research Collaboration. IEEE Computer Graphics and Applications, vol. 21, no. 1, pp. 37-43, 2001.
19. Fletcher J.D.Education and Training Technology in the Military. science, vol. 323, no. 5910, pp. 72-75, 2009.
20. Proctor, M.D. and Woodman, M.D.Training “Shoot House” Tactics using a Game. The Journal of Defense Modeling and Simulation, vol. 4, no. 1, pp. 55-63, 2007.
21. Zhang L., Jack L.B., andNandi A.K.Fault Detection using Genetic Programming. Mechanical Systems and Signal Processing, vol. 19, no. 2, pp. 271-289, 2005.
22. Werle P., Slemr F., Maurer K., Kormann R., Mücke R., andJänker B.Near-and Mid-Infrared Laser-Optical Sensors for Gas Analysis. Optics and lasers in engineering, vol.37, no. 2-3, pp. 101-114, 2002.
23. Gil E., Escolà A., Rosell J.R., Planas S., andVal L.Variable Rate Application of Plant Protection Products in Vineyard using Ultrasonic Sensors. Crop Protection, vol. 26, no. 8, pp. 1287-1297, 2007.
24. Chon H.D., Jun S., Jung H., andAn S.W. using RFID for Accurate Positioning. Positioning, vol. 1, no. 08, 2004.
25. Steer M.IEEE Transactions on Microwave Theory and Techniques. IEEE Microwave Magazine, vol. 7, no. 1, pp. 66-72, 2006.
26. Hu Y., Yan Y., Liang J., andWang L.A Miniature, Low-Cost MEMS AHRS with Application to Posture Control of Robotic Fish. In2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, pp. 1392-1395, 2013.
27. Caron F., Duflos E., Pomorski D., andVanheeghe P.GPS/IMU Data Fusion using Multisensor Kalman Filtering: Introduction of Contextual Aspects. Information fusion, vol. 7, no. 2, pp. 221-230, 2006.
28. Brückner H.P., Spindeldreier C., andBlume H.Modification and Fixed-Point Analysis of a Kalman Filter for Orientation Estimation based on 9D Inertial Measurement Unit Data. In2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 3953-3956, 2013.
29. Zihajehzadeh S., Loh D., Lee M., Hoskinson R., andPark E.J.A Cascaded Two-Step Kalman Filter for Estimation of Human Body Segment Orientation using MEMS-IMU. In2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp. 6270-6273, 2014.
30. Cui L., Wang X., Xu Y., Jiang H., andZhou J.A Novel Switching Unscented Kalman Filter Method for Remaining Useful Life Prediction of Rolling Bearing. Measurement, vol. 135, pp. 678-684, 2019.
31. Nino-Ruiz, E.D., Sandu, A., and Deng, X. A Parallel Implementation of the Ensemble Kalman Filter based on Modified Cholesky Decomposition. Journal of Computational Science, vol. 36, pp. 100654, 2019.
32. Rhudy M.B., Salguero R.A., andHolappa K.A Kalman Filtering Tutorial for Undergraduate Students. International Journal of Computer Science & Engineering Survey, vol. 8, no. 1, pp. 1-9, 2017.
33. Chiu, Y.P. and Shiau, Y.C.Study on the Application of Unity Software in Emergency Evacuation Simulation for Elder. Artificial Life and Robotics, vol. 21, pp. 232-238, 2016.
34. Patil, G.V. and Deshpande, S.L.Distributed Rendering System for 3D Animations with Blender. In2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), IEEE, pp. 91-98, 2016.
35. Casterson S.Oculus Rift: A Beginner’s Guide, vol. 1, Conceptual Kings, 2016.
36. Borrego A., Latorre J., Alcañiz M., andLlorens R.Comparison of Oculus Rift and HTC Vive: Feasibility for Virtual Reality-Based Exploration, Navigation, Exergaming, and Rehabilitation. Games for health journal, vol. 7, no. 3, pp. 151-156, 2018.

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