Lidar-based Avoidance

LiDAR-Based Avoidance is a robotic navigation strategy that uses light detection and ranging (LiDAR) sensor data to detect, localize, and dynamically circumvent obstacles in real time, distinguishing itself from other avoidance methods through its reliance on high-resolution, three-dimensional environmental point cloud measurements.

37

Publications

1.5K

Citations

158

Authors

49

Institutions

Publications per year

2017–2026

21

Authors

158

  1. RS
    Roberto Sabatini

    RMIT University, Australia

    4 publications · 230 citations

    4

    h-index

  2. AG
    Alessandro Gardi

    RMIT University, Australia

    4 publications · 230 citations

    4

    h-index

  3. AJ
    Andrew Johnson

    Jet Propulsion Laboratory, United States

    3 publications · 222 citations

    3

    h-index

  4. UP
    Umberto Papa

    Parthenope University of Naples, Italy

    2 publications · 36 citations

    2

    h-index

  5. JL
    Jiechao Liu

    University of Michigan, United States

    2 publications · 73 citations

    2

    h-index

1–5 of 158

Venues

  1. Remote Sensing

    2 publications · 91 citations

    2

    h-index

  2. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 2 publications · 92 citations

    2

    h-index

  3. Journal of Intelligent & Fuzzy Systems

    1 publications · 16 citations

    1

    h-index

  4. AIAA SPACE 2013 Conference and Exposition

    Conference · 1 publications · 14 citations

    1

    h-index

  5. 1

    h-index

1–5