Outside-in monocular IR camera based HMD pose estimation via geometric optimization

Pavel A. Savkin, Shunsuke Saito, Jarich Vansteenberge, Tsukasa Fukusato, Lochlainn Wilson, Shigeo Morishima

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Accurately tracking a Head Mounted Display (HMD) with a 6 degree of freedom is essential to achieve a comfortable and a nausea free experience in Virtual Reality. Existing commercial HMD systems using synchronized Infrared (IR) camera and blinking IR-LEDs can achieve highly accurate tracking. However, most of the o-the-shelf cameras do not support frame synchronization. In this paper, we propose a novel method for real time HMD pose estimation without using any camera synchronization or LED blinking. We extended over the state of the art pose estimation algorithm by introducing geometrically constrained optimization. In addition, we propose a novel system to increase robustness to the blurred IR-LEDs paerns appearing at high-velocity movements. The quantitative evaluations showed signicant improvements in pose stability and accuracy over wide rotational movements as well as a decrease in runtime.

    Original languageEnglish
    Title of host publicationProceedings - VRST 2017
    Subtitle of host publication23rd ACM Conference on Virtual Reality Software and Technology
    PublisherAssociation for Computing Machinery
    VolumePart F131944
    ISBN (Electronic)9781450355483
    DOIs
    Publication statusPublished - 2017 Nov 8
    Event23rd ACM Conference on Virtual Reality Software and Technology, VRST 2017 - Gothenburg, Sweden
    Duration: 2017 Nov 82017 Nov 10

    Other

    Other23rd ACM Conference on Virtual Reality Software and Technology, VRST 2017
    CountrySweden
    CityGothenburg
    Period17/11/817/11/10

    Fingerprint

    Light emitting diodes
    Cameras
    Display devices
    Infrared radiation
    Synchronization
    Constrained optimization
    Virtual reality

    Keywords

    • Monocular IR camera
    • Perspective-n-Point problem
    • Position tracking
    • Vision-based pose estimation

    ASJC Scopus subject areas

    • Software

    Cite this

    Savkin, P. A., Saito, S., Vansteenberge, J., Fukusato, T., Wilson, L., & Morishima, S. (2017). Outside-in monocular IR camera based HMD pose estimation via geometric optimization. In Proceedings - VRST 2017: 23rd ACM Conference on Virtual Reality Software and Technology (Vol. Part F131944). [a7] Association for Computing Machinery. https://doi.org/10.1145/3139131.3139136

    Outside-in monocular IR camera based HMD pose estimation via geometric optimization. / Savkin, Pavel A.; Saito, Shunsuke; Vansteenberge, Jarich; Fukusato, Tsukasa; Wilson, Lochlainn; Morishima, Shigeo.

    Proceedings - VRST 2017: 23rd ACM Conference on Virtual Reality Software and Technology. Vol. Part F131944 Association for Computing Machinery, 2017. a7.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Savkin, PA, Saito, S, Vansteenberge, J, Fukusato, T, Wilson, L & Morishima, S 2017, Outside-in monocular IR camera based HMD pose estimation via geometric optimization. in Proceedings - VRST 2017: 23rd ACM Conference on Virtual Reality Software and Technology. vol. Part F131944, a7, Association for Computing Machinery, 23rd ACM Conference on Virtual Reality Software and Technology, VRST 2017, Gothenburg, Sweden, 17/11/8. https://doi.org/10.1145/3139131.3139136
    Savkin PA, Saito S, Vansteenberge J, Fukusato T, Wilson L, Morishima S. Outside-in monocular IR camera based HMD pose estimation via geometric optimization. In Proceedings - VRST 2017: 23rd ACM Conference on Virtual Reality Software and Technology. Vol. Part F131944. Association for Computing Machinery. 2017. a7 https://doi.org/10.1145/3139131.3139136
    Savkin, Pavel A. ; Saito, Shunsuke ; Vansteenberge, Jarich ; Fukusato, Tsukasa ; Wilson, Lochlainn ; Morishima, Shigeo. / Outside-in monocular IR camera based HMD pose estimation via geometric optimization. Proceedings - VRST 2017: 23rd ACM Conference on Virtual Reality Software and Technology. Vol. Part F131944 Association for Computing Machinery, 2017.
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