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      <a class="brand" href="/">Won Ko</a>
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        <div class="name">Won Ko</div>
        <div class="title">AI/ML • Robotics and Control • Mechatronics • Sensor Fusion • Computer Vision • SLAM</div>

        <ul class="meta">
          <li><i class="fa-solid fa-location-dot"></i><span>Seoul, South Korea</span></li>
          <li><i class="fa-solid fa-building-columns"></i>
            <span><span>Senior Researcher</span><br><span>Mobyus Future Tech R&amp;D Lab</span></span>
          </li>
          <li><i class="fa-solid fa-building-columns"></i>
            <span><span>M.S in Computer Engineering</span><br><span>UC Santa Cruz</span></span>
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          <li><i class="fa-solid fa-building-columns"></i>
            <span><span>B.S in Computer Engineering</span><br><span>UC Santa Cruz</span></span>
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          <li><i class="fa-solid fa-envelope"></i><span><a href="mailto:kowon861@gmail.com">kowon861@gmail.com</a></span></li>
          <li><i class="fa-brands fa-github"></i><span><a href="https://github.com/Won-Ko">GitHub</a></span></li>
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      <section class="card main">
        <h1>About</h1>

        <p class="lead">
          I’m a <b>Senior Researcher</b> building autonomy and 3D perception systems for mobile robots—multi-sensor fusion,
          state estimation (SLAM), and motion planning/control—with an emphasis on reliable behavior on real hardware.
        </p>

        <p class="results">
          <b>Recent results:</b> ≤30 mm 3D SLAM position error (testbeds); pallet-hole detection at 4 m with ~0.5° angular error.
        </p>

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          I chose robotics because it lives at the intersection of algorithms and reality: the math can be clean,
          but deployment never is.
        </p>

        <hr class="section-divider">

        <h2>Current focus</h2>
        <ul>
          <li><b>3D SLAM:</b> LiDAR–inertial odometry (LIO) and visual–inertial odometry (VIO/VSLAM) for robust localization and mapping.</li>
          <li><b>Sensor calibration algorithms:</b> LiDAR↔IMU extrinsic calibration, Camera↔IMU extrinsic calibration, and joint calibration across LiDAR, IMU, and camera.</li>
          <li><b>Navigation:</b> integrating perception + estimation into planning/control for safe, repeatable motion in dynamic environments.</li>
          <li><b>Perception:</b> camera + LiDAR fusion for obstacle detection and scene understanding to support navigation and measurement tasks.</li>
          <li><b>System validation:</b> prototyping and evaluation in ROS 2 with a focus on measurable accuracy and real-world reliability.</li>
        </ul>

        <hr class="section-divider">

        <h2>Research interests</h2>
        <ul>
          <li><b>Autonomous systems:</b> building reliable autonomy that connects perception, estimation, and decision-making with measurable safety and robustness in real deployments.</li>

          <li><b>Guidance, Navigation &amp; Control (GNC):</b> motion control and navigation under uncertainty, including trajectory tracking, system modeling, and robust control for real platforms.</li>

          <li><b>Multi-modal sensor fusion for 3D measurement:</b> camera/depth/LiDAR/radar/IMU fusion with calibration, time synchronization, and uncertainty-aware estimation.</li>

          <li><b>3D perception for precision tasks:</b> detection, segmentation, keypoints, dense depth, and reconstruction with quantitative evaluation (accuracy, latency, robustness).</li>

          <li><b>Perception-aware planning &amp; safety:</b> decision-making under uncertainty (belief-aware / stochastic ideas) paired with verifiable safety mechanisms (e.g., safety shields / CBF-style constraints).</li>

          <li><b>Spatial AI roadmap (future direction):</b> <span class="roadmap">VSLAM → 3D space reconstruction → Spatial AI → VLM/VLA</span>
            to ground semantic understanding in 3D and enable reliable vision–language–action for <b>inspection</b> and <b>precision measurement</b> workflows.</li>

          <li><b>Sim-to-real robustness:</b> domain randomization, data-efficient adaptation, and active learning to reduce retuning in changing environments.</li>
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