Tom Duckett

About

Tom Duckett is a leading robotics researcher whose work spans mobile robot navigation, long-term autonomy, and agricultural robotics. Based at the University of Lincoln, he has made foundational contributions to some of the most challenging problems in autonomous systems. His 2007 paper on 3D Normal Distributions Transform (3D-NDT) scan registration, now cited over 760 times, introduced a highly influential algorithm for robot mapping using range finder data that remains widely used in autonomous vehicle research. His early work on dynamic maps and SLAM algorithms — each accumulating nearly 300 citations — addressed how robots can build and maintain accurate representations of changing environments over time. Duckett also led or contributed to major research initiatives including the STRANDS Project, which demonstrated long-term robot autonomy in real-world service environments, and pioneered gas sensing and chemical mapping with mobile robots. In recent years, he has become a prominent voice in agricultural robotics, co-authoring a widely read 2018 EPSRC white paper on the field's future and contributing to deep learning approaches for crop-weed segmentation. With over 2,700 citations across his most impactful works, Duckett's research bridges foundational robotics and transformative real-world applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Scan registration for autonomous mining vehicles using 3D‐NDT

   767 citations · 2007
2. 2
   Agricultural Robotics: The Future of Robotic Agriculture

   362 citations · 2018
3. 3
   Dynamic Maps for Long-Term Operation of Mobile Service Robots

   296 citations · 2005
4. 4
   A multilevel relaxation algorithm for simultaneous localization and mapping

   288 citations · 2005
5. 5
   A Practical Multirobot Localization System

   210 citations · 2014
6. 6
   The STRANDS Project: Long-Term Autonomy in Everyday Environments

   196 citations · 2017
7. 7
   Building gas concentration gridmaps with a mobile robot

   188 citations · 2004
8. 8
   Airborne Chemical Sensing with Mobile Robots

   165 citations · 2006
9. 9
   Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

   161 citations · 2019
10. 10
    FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments

    154 citations · 2017

Key Collaborators

Contact & Links