Sensor Fusion Optimization

Sensor Fusion Optimization is a computational framework that integrates and refines multimodal sensor data through algorithmic techniques to enhance the accuracy, latency, and robustness of augmented reality systems by minimizing redundancy and maximizing complementary information across heterogeneous sensing sources.

30

Publications

2.4K

Citations

111

Authors

49

Institutions

Publications per year

2017–2026

14

Authors

111

  1. XY
    Xinyu Yang

    Xi'an Jiaotong University, China

    2 publications · 1K citations

    2

    h-index

  2. HM
    Huizi Mao

    Stanford University, United States

    2 publications · 1K citations

    2

    h-index

  3. DR
    Daniela Rus

    Massachusetts Institute of Technology, United States

    2 publications · 1K citations

    2

    h-index

  4. AA
    Alexander Amini

    Massachusetts Institute of Technology, United States

    2 publications · 1K citations

    2

    h-index

  5. ZL
    Zhijian Liu

    Massachusetts Institute of Technology, United States

    2 publications · 1K citations

    2

    h-index

1–5 of 111

Venues

  1. Sensors

    2 publications · 105 citations

    3

    h-index

  2. 1

    h-index

  3. Fusion Engineering and Design

    1 publications · 10 citations

    1

    h-index

  4. Assembly Automation

    1 publications · 10 citations

    1

    h-index

  5. 1

    h-index

1–5