Visual Teach-and-repeat Navigation

Visual Teach-And-Repeat Navigation is a visual navigation framework that enables a robot to autonomously retrace a previously demonstrated route in an environment using only camera inputs, distinguished by its reliance on visual memory of a taught trajectory without requiring explicit metric maps or external localization systems.

26

Publications

1K

Citations

64

Authors

21

Institutions

Publications per year

2017–2026

14

Variants

Teach-and-repeat Navigation

Authors

64

  1. TD
    Timothy D. Barfoot

    University of Toronto, Canada

    12 publications · 600 citations

    11

    h-index

  2. MP
    Michael Paton

    University of Toronto, Canada

    4 publications · 153 citations

    4

    h-index

  3. TK
    Tomáš Krajník

    Czech Technical University in Prague, Czechia

    4 publications · 117 citations

    4

    h-index

  4. PF
    Paul Furgale

    ETH Zurich, Switzerland

    3 publications · 325 citations

    3

    h-index

  5. MW
    Michael Warren

    Queensland University of Technology, Australia

    3 publications · 153 citations

    3

    h-index

1–5 of 64

Venues

  1. Journal of Field Robotics

    JFR · 4 publications · 339 citations

    4

    h-index

  2. IEEE Robotics and Automation Letters

    3 publications · 101 citations

    3

    h-index

  3. Sensors

    2 publications · 50 citations

    2

    h-index

  4. IEEE Access

    1 publications · 96 citations

    1

    h-index

  5. Robotics and Autonomous Systems

    1 publications · 59 citations

    1

    h-index

1–5