Probabilistic Robot Localization

Probabilistic Robot Localization is a computational framework within robot localization that represents the robot’s belief about its position as a probability distribution over possible states, distinguishing itself from deterministic methods by explicitly accounting for uncertainty in both sensor measurements and motion actions through recursive Bayesian estimation.

215

Publications

19.4K

Citations

541

Authors

192

Institutions

Publications per year

2017–2026

36

Variants

Probabilistic Localization

Authors

541

  1. WB
    Wolfram Burgard

    University of Freiburg, Germany

    20 publications · 7.1K citations

    17

    h-index

  2. ST
    Sebastian Thrun

    Carnegie Mellon University, United States

    16 publications · 7.2K citations

    14

    h-index

  3. DF
    Dieter Fox

    University of Washington, United States

    6 publications · 3.9K citations

    6

    h-index

  4. FC
    Fernando Caballero

    Universidad de Sevilla, Spain

    5 publications · 218 citations

    5

    h-index

  5. FD
    Frank Dellaert

    Georgia Institute of Technology, United States

    5 publications · 4.6K citations

    5

    h-index

1–5 of 541

Venues

  1. Robotics and Autonomous Systems

    8 publications · 651 citations

    8

    h-index

  2. IEEE Transactions on Robotics

    7 publications · 802 citations

    7

    h-index

  3. Sensors

    7 publications · 284 citations

    7

    h-index

  4. The International Journal of Robotics Research

    IJRR · 6 publications · 338 citations

    6

    h-index

  5. Journal of Intelligent & Robotic Systems

    4 publications · 67 citations

    4

    h-index

1–5