Probabilistic Perception

Probabilistic Perception is a computational framework within multi-robot perception that models sensory interpretation and environmental state estimation under uncertainty using probability theory to represent and reason about ambiguity in sensor data, robotpose, and dynamic environments.

72

Publications

5.2K

Citations

199

Authors

105

Institutions

Publications per year

2017–2026

17

Authors

199

  1. ÖC
    Ö. Ciftcioglu

    Delft University of Technology, Netherlands

    4 publications · 82 citations

    4

    h-index

  2. MS
    Michael S. Bittermann

    Delft University of Technology, Netherlands

    4 publications · 82 citations

    4

    h-index

  3. IS
    I.S. Sariyildiz

    Delft University of Technology, Netherlands

    3 publications · 59 citations

    3

    h-index

  4. MB
    Maren Bennewitz

    University of Freiburg, Germany

    2 publications · 551 citations

    2

    h-index

  5. CL
    Christian Laugier

    Institut national de recherche en sciences et technologies du numérique, France

    2 publications · 60 citations

    2

    h-index

1–5 of 199

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 315 citations

    4

    h-index

  2. Vision Research

    2 publications · 255 citations

    2

    h-index

  3. The International Journal of Robotics Research

    IJRR · 2 publications · 558 citations

    2

    h-index

  4. PLoS Computational Biology

    2 publications · 112 citations

    2

    h-index

  5. Medical Entomology and Zoology

    2 publications · 401 citations

    2

    h-index

1–5