Daniele De Martini is Associate Professor in Mobile Robotics at the Oxford Robotics Institute and the Oxford e-Research Centre, University of Oxford, and Tutorial Fellow in Engineering Science at Keble College. He co-leads the Mobile Robotics Group alongside Professor Paul Newman. His research combines robotics and artificial intelligence to design autonomous systems capable of perception, mapping, localisation, and adaptive decision-making. By exploiting multiple sensing modalities—from vision to lidar to radar—his group explores how robots can reliably navigate and interpret the world, even in challenging or extreme conditions. Daniele has deployed robots in environments ranging from urban Oxford to the Scottish Highlands. Another key aspect of his work examines robotics–infrastructure interaction, enabling dynamic sharing of sensing and computing resources between robots and smart environments to support safe and scalable autonomy.
Recent Publications
AutoInspect: towards long-term autonomous inspection and monitoring
Staniaszek M, Flatscher T, Rowell J, Niu H, Liu W et al. (2025), IEEE Transactions on Field Robotics
BibTeX
@article{autoinspecttowa-2025/7,
title={AutoInspect: towards long-term autonomous inspection and monitoring},
author={Staniaszek M, Flatscher T, Rowell J, Niu H, Liu W et al.},
journal={IEEE Transactions on Field Robotics},
publisher={IEEE},
year = "2025"
}
The Oxford RobotCycle Project: A Multimodal Urban Cycling Dataset for Assessing the Safety of Vulnerable Road Users
Panagiotaki E, Thuremella D, Baghabrah J, Sze S, Fu LFT et al. (2025), IEEE Transactions on Field Robotics, PP(99), 1-1
BibTeX
@article{theoxfordrobotc-2025/5,
title={The Oxford RobotCycle Project: A Multimodal Urban Cycling Dataset for Assessing the Safety of Vulnerable Road Users},
author={Panagiotaki E, Thuremella D, Baghabrah J, Sze S, Fu LFT et al.},
journal={IEEE Transactions on Field Robotics},
volume={PP},
pages={1-1},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
year = "2025"
}
Bayesian Radar Cosplace: Directly estimating location uncertainty in radar place recognition
Agarwal S, Yuan J, Newman P, De Martini D & Gadd M (2025), IET Radar Sonar & Navigation, 19(1)
BibTeX
@article{bayesianradarco-2025/3,
title={Bayesian Radar Cosplace: Directly estimating location uncertainty in radar place recognition},
author={Agarwal S, Yuan J, Newman P, De Martini D & Gadd M},
journal={IET Radar Sonar & Navigation},
volume={19},
publisher={Institution of Engineering and Technology (IET)},
year = "2025"
}
Tiny Lidars for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments
Caroleo G, Albini A, De Martini D, Barfoot TD & Maiolino P (2025)
BibTeX
@inproceedings{tinylidarsforma-2025/3,
title={Tiny Lidars for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments},
author={Caroleo G, Albini A, De Martini D, Barfoot TD & Maiolino P},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = "2025"
}
NeuralFloors++: consistent street-level scene generation from BEV semantic maps
Musat V, De Martini D, Gadd M & Newman P (2024), 12872-12879
BibTeX
@inproceedings{neuralfloorscon-2024/12,
title={NeuralFloors++: consistent street-level scene generation from BEV semantic maps},
author={Musat V, De Martini D, Gadd M & Newman P},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
pages={12872-12879},
year = "2024"
}
GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation
Panagiotaki E, Pramatarov G, Kunze L & Martini DD (2024)
BibTeX
@inproceedings{graphsceneondem-2024/10,
title={GraphSCENE: On-Demand Critical Scenario Generation for Autonomous
Vehicles in Simulation},
author={Panagiotaki E, Pramatarov G, Kunze L & Martini DD},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = "2024"
}
NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms
Panagiotaki E, De Martini D, Kunze L & Veličković P (2024)
BibTeX
@misc{naricpneuralexe-2024/10,
title={NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms},
author={Panagiotaki E, De Martini D, Kunze L & Veličković P},
year = "2024"
}
Robot-relay:building-wide, calibration-less visual servoing with learned sensor handover networks
Robinson L, Gadd M, Newman P & De Martini D (2024), Experimental Robotics: The 18th International Symposium, 129-140
BibTeX
@inproceedings{robotrelaybuild-2024/8,
title={Robot-relay:building-wide, calibration-less visual servoing with learned sensor handover networks},
author={Robinson L, Gadd M, Newman P & De Martini D},
booktitle={18th International Symposium on Experimental Robotics (ISER 2023)},
pages={129-140},
year = "2024"
}
VDNA-PR: using general dataset representations for robust sequential visual place recognition
Ramtoula B, De Martini D, Gadd M & Newman P (2024), 2024 IEEE International Conference on Robotics and Automation (ICRA), 15883-15889
BibTeX
@inproceedings{vdnaprusinggene-2024/8,
title={VDNA-PR: using general dataset representations for robust sequential visual place recognition},
author={Ramtoula B, De Martini D, Gadd M & Newman P},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA 2024)},
pages={15883-15889},
year = "2024"
}
What you see is what you get: experience ranking with deep neural dataset-to-dataset similarity for topological localisation
Gadd M, Ramtoula B, De Martini D & Newman P (2024), Experimental Robotics: The 18th International Symposium, 595-607
BibTeX
@inproceedings{whatyouseeiswha-2024/8,
title={What you see is what you get: experience ranking with deep neural dataset-to-dataset similarity for topological localisation},
author={Gadd M, Ramtoula B, De Martini D & Newman P},
booktitle={18th International Symposium on Experimental Robotics (ISER 2023)},
pages={595-607},
year = "2024"
}
