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Position Based Camera-2D LiDAR Fusion and Person Following for Mobile Robots.

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    Position-based tracking using multiple sensors is more effective for mobile robot person following than image-based methods. This approach reduces errors and ID switching, ensuring reliable tracking in real-world applications.

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    Area of Science:

    • Robotics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • 2D LiDAR tracking is common for mobile robot person following but has limitations.
    • Existing advanced methods combine position and appearance data, categorized as position-based or image-based tracking.

    Purpose of the Study:

    • To evaluate a position-based target person tracking system on a real robot for person following.
    • To compare the performance of position-based tracking against image-based tracking (SORT algorithms).

    Main Methods:

    • Developed a position-based tracking system using deep learning for person detection.
    • Implemented multi-sensor fusion with RGBD cameras and LiDAR.
    • Utilized the UCMCtrack algorithm for tracking and compared it with SORT algorithms.

    Main Results:

    • Position-based tracking demonstrated superior suitability for the person following application on a real robot.
    • Image-based methods exhibited a tendency for ID switching, especially in scenarios with close image proximity but 3D spatial separation.

    Conclusions:

    • Position-based tracking is more robust and reliable for person following applications compared to image-based methods.
    • The developed system effectively addresses limitations of traditional methods, offering consistent tracking without ID switches.