Monte Carlo Localization

Monte Carlo Localization is a probabilistic localization algorithm that belongs to the class of sampling-based methods, distinguished by its use of a particle filter to approximate a robot’s posterior probability distribution over its pose in an environment through repeated sampling and weighting based on sensor observations.

376

Publications

22.6K

Citations

972

Authors

359

Institutions

Publications per year

2017–2026

118

Authors

972

  1. WB
    Wolfram Burgard

    University of Freiburg, Germany

    15 publications · 5.5K citations

    14

    h-index

  2. CS
    Cyrill Stachniss

    University of Bonn, Germany

    8 publications · 487 citations

    8

    h-index

  3. FC
    Fernando Caballero

    Universidad de Sevilla, Spain

    7 publications · 286 citations

    7

    h-index

  4. ST
    Sebastian Thrun

    Carnegie Mellon University, United States

    6 publications · 5.2K citations

    6

    h-index

  5. GD
    Gian Diego Tipaldi

    University of Freiburg, Germany

    5 publications · 380 citations

    5

    h-index

1–5 of 972

Venues

  1. Sensors

    15 publications · 540 citations

    13

    h-index

  2. Robotics and Autonomous Systems

    7 publications · 255 citations

    7

    h-index

  3. IEEE Transactions on Robotics

    6 publications · 752 citations

    6

    h-index

  4. IEEE Robotics and Automation Letters

    5 publications · 130 citations

    6

    h-index

  5. IEEE Access

    5 publications · 96 citations

    5

    h-index

1–5