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    This study introduces a novel pose estimation method using two-graph simultaneous localization and mapping (SLAM) to improve indoor navigation for the visually impaired. The system enhances accuracy and reduces computational time for a better wayfinding experience.

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    Area of Science:

    • Robotics
    • Computer Vision
    • Human-Computer Interaction

    Background:

    • Visually impaired individuals face significant challenges with indoor navigation.
    • Existing indoor wayfinding systems often lack the required accuracy and efficiency.
    • Accurate pose estimation is crucial for effective spatial awareness and navigation.

    Purpose of the Study:

    • To develop a robust 6-degree of freedom (DOF) pose estimation (PE) method.
    • To create an indoor wayfinding system for the visually impaired based on the PE method.
    • To reduce accumulated pose error and enhance navigation accuracy in indoor environments.

    Main Methods:

    • Implemented a two-graph simultaneous localization and mapping (SLAM) approach.
    • Integrated floor plane extraction for 6-DOF SLAM to minimize roll, pitch, and Z errors.
    • Incorporated wall line extraction for 3-DOF SLAM to reduce X, Y, and yaw errors.

    Main Results:

    • The proposed PE method significantly reduces 6-DOF pose error compared to state-of-the-art planar SLAM methods.
    • The system achieves higher pose accuracy with reduced computational time.
    • Experimental results confirm the effectiveness of the PE method and the wayfinding system.

    Conclusions:

    • The developed PE method offers a more accurate and computationally efficient solution for indoor localization.
    • The wayfinding system effectively assists visually impaired users in indoor navigation tasks.
    • This technology has the potential to substantially improve independence and accessibility for the visually impaired.