World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
52
Citations
9126
World Ranking
5152
National Ranking
307

Tom Duckett publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tom Duckett sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 181 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Tom Duckett D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tom Duckett sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 52 D-Index — 65th percentile

65% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Tom Duckett is affiliated with the University of Lincoln in the United Kingdom. Their research spans engineering and computer science, with a primary focus on robotics and sensor-based localization, advanced vision and imaging, and smart agriculture and AI.

Their work touches on several specialized subfields, including computer vision and pattern recognition, aerospace engineering, environmental engineering, plant science, and mechanical engineering. The scientist has addressed topics such as video surveillance and tracking methods, remote sensing and LiDAR applications, advanced image and video retrieval techniques, and soil moisture and remote sensing.

Tom Duckett has contributed to numerous research papers published in various venues, reflecting an engagement with both academic journals and conference proceedings. Some of their recent publications include:

  • Robot perception of static and dynamic objects with an autonomous floor scrubber, 2020, published in Intelligent Service Robotics
  • Kriging-based robotic exploration for soil moisture mapping using a cosmic-ray sensor, 2020, published in Lincoln Repository (University of Lincoln)
  • Robotic Exploration for Learning Human Motion Patterns, 2021, published in IEEE Transactions on Robotics
  • Robust and Long-term Monocular Teach and Repeat Navigation using a Single-experience Map, 2021, presented at the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Estimating soil aggregate size distribution from images using pattern spectra, 2020, published in Biosystems Engineering

Their research has appeared frequently in venues such as arXiv (Cornell University), Lincoln Repository (University of Lincoln), 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Journal of Robotics & Autonomous Systems, and Intelligent Service Robotics.

Collaboration is a significant part of Tom Duckett's work. Frequent coauthors include:

  • Zhi Yan
  • Grzegorz Cielniak
  • Li Sun
  • Tomáš Krajník
  • Nicola Bellotto

Best Publications

  • Scan registration for autonomous mining vehicles using 3D-NDT

    Martin Magnusson;Achim J. Lilienthal;Tom Duckett

  • A multilevel relaxation algorithm for simultaneous localization and mapping

    U. Frese;P. Larsson;T. Duckett

  • A Practical Multirobot Localization System

    Tomáš Krajník;Matías Nitsche;Jan Faigl;Petr Vanĕk

  • Dynamic maps for long-term operation of mobile service robots

    Peter Biber;Tom Duckett

  • Airborne chemical sensing with mobile robots

    Achim J. Lilienthal;Amy Loutfi;Tom Duckett

  • The STRANDS Project: Long-Term Autonomy in Everyday Environments

    Nick Hawes;Christopher Burbridge;Ferdian Jovan;Lars Kunze

  • 3D modeling of indoor environments by a mobile robot with a laser scanner and panoramic camera

    P. Biber;H. Andreasson;T. Duckett;A. Schilling

  • Fast, On-Line Learning of Globally Consistent Maps

    Tom Duckett;Stephen Marsland;Jonathan Shapiro

  • Learning globally consistent maps by relaxation

    T. Duckett;S. Marsland;J. Shapiro

  • Creating gas concentration gridmaps with a mobile robot

    A. Lilienthal;T. Duckett

  • Artificial Intelligence for Long-Term Robot Autonomy: A Survey

    Lars Kunze;Nick Hawes;Tom Duckett;Marc Hanheide

  • FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments

    Tomas Krajnik;Jaime P. Fentanes;Joao M. Santos;Tom Duckett

  • Localization for Mobile Robots using Panoramic Vision, Local Features and Particle Filter

    H. Andreasson;A. Treptow;T. Duckett

  • Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

    Petra Bosilj;Erchan Aptoula;Tom Duckett;Grzegorz Cielniak

  • Localization of mobile robots with omnidirectional vision using Particle Filter and iterative SIFT

    Hashem Tamimi;Henrik Andreasson;André Treptow;Tom Duckett

  • Online learning for human classification in 3D LiDAR-based tracking

    Zhi Yan;Tom Duckett;Nicola Bellotto

  • An adaptive appearance-based map for long-term topological localization of mobile robots

    F. Dayoub;T. Duckett

  • A genetic algorithm for simultaneous localization and mapping

    T. Duckett

  • 3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

    Li Sun;Zhi Yan;Sergi Molina Mellado;Marc Hanheide

  • A Novel Weakly-Supervised Approach for RGB-D-Based Nuclear Waste Object Detection

    Li Sun;Cheng Zhao;Zhi Yan;Pengcheng Liu

  • Incremental Spectral Clustering and Its Application To Topological Mapping

    C. Valgren;T. Duckett;A. Lilienthal

Frequent Co-Authors

Tomas Krajnik
Tomas Krajnik Czech Technical University in Prague
Achim J. Lilienthal
Achim J. Lilienthal Technical University of Munich
Henrik Andreasson
Henrik Andreasson Örebro University
Rustam Stolkin
Rustam Stolkin University of Birmingham
Alessandro Saffiotti
Alessandro Saffiotti Örebro University
Andreas Zell
Andreas Zell University of Tübingen
Gerhard Neumann
Gerhard Neumann Karlsruhe Institute of Technology
Michael Wand
Michael Wand Johannes Gutenberg University of Mainz
Bastian Leibe
Bastian Leibe RWTH Aachen University
Patric Jensfelt
Patric Jensfelt Royal Institute of Technology

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