Robot Pose Tracking

Robot Pose Tracking is a methodological framework within the field of robotics that distinguishes itself by estimating a robot's position and orientation over time in an environment through sequential Bayesian inference, specifically by maintaining and updating a probabilistic belief distribution over possible poses using sensor observations and motion models.

99

Publications

4.7K

Citations

306

Authors

121

Institutions

Publications per year

2017–2026

41

Authors

306

  1. AZ
    Andreas Zell

    University of Tübingen, Germany

    3 publications · 41 citations

    3

    h-index

  2. HI
    Henrik I. Christensen

    KTH Royal Institute of Technology, Sweden

    3 publications · 270 citations

    3

    h-index

  3. BR
    Bodo Rosenhahn

    Leibniz University Hannover, Germany

    2 publications · 175 citations

    2

    h-index

  4. JH
    Jui‐Cheng Hsu

    National Chung Hsing University, Taiwan

    2 publications · 84 citations

    2

    h-index

  5. SY
    Shaowu Yang

    National University of Defense Technology, China

    2 publications · 30 citations

    2

    h-index

1–5 of 306

Venues

  1. IEEE Robotics and Automation Letters

    3 publications · 186 citations

    4

    h-index

  2. IEEE Access

    3 publications · 82 citations

    4

    h-index

  3. IEEE Transactions on Robotics and Automation

    3 publications · 559 citations

    3

    h-index

  4. Sensors

    2 publications · 47 citations

    3

    h-index

  5. arXiv (Cornell University)

    Repository · 3 publications · 163 citations

    3

    h-index

1–5