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Related Concept Videos

Passive Filters01:27

Passive Filters

523
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Adaptive Complementary Filter for Hybrid Inside-Out Outside-In HMD Tracking With Smooth Transitions.

Riccardo Monica, Dario Lodi Rizzini, Jacopo Aleotti

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    This study introduces an adaptive complementary filter for hybrid head-mounted display (HMD) tracking in virtual reality. It ensures smooth transitions during tracking signal recovery, improving user comfort and reducing errors.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Robotics

    Background:

    • Head-mounted displays (HMDs) in virtual reality (VR) commonly use inside-out tracking (visual SLAM).
    • Outside-in systems (e.g., motion capture) offer alternative tracking but are prone to occlusion and blind spots.
    • Hybrid tracking combines inside-out and outside-in methods to overcome individual limitations.

    Purpose of the Study:

    • To develop a sensor fusion algorithm for hybrid HMD tracking that ensures smooth transitions between tracking modes.
    • To mitigate user discomfort caused by tracking discontinuities when outside-in signals are recovered.
    • To provide a configurable solution balancing user experience and tracking accuracy.

    Main Methods:

    • Proposed an adaptive complementary filter for sensor fusion in hybrid HMD tracking.
    • Implemented a user study in a room-scale VR environment with simulated outside-in tracking signal losses.
    • Evaluated the system's performance against an Extended Kalman Filter and a state-of-the-art complementary filter.

    Main Results:

    • The adaptive complementary filter demonstrated improved user experience compared to the Extended Kalman Filter.
    • Tracking error was reduced compared to a standard complementary filter when maintaining similar user experience quality.
    • The proposed method offers configurable parameters for balancing user experience and tracking error.

    Conclusions:

    • The adaptive complementary filter effectively enables smooth transitions in hybrid HMD tracking systems.
    • This approach enhances user comfort in VR by preventing jarring viewpoint changes.
    • The method provides a flexible solution for optimizing VR tracking performance.