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Daniele De Martini
Daniele De Martini
Departmental Lecturer in Mobile Robotics, Oxford Robotics Institute
Verified email at robots.ox.ac.uk
Title
Cited by
Cited by
Year
Online fall detection using recurrent neural networks on smart wearable devices
M Musci, D De Martini, N Blago, T Facchinetti, M Piastra
IEEE Transactions on Emerging Topics in Computing 9 (3), 1276-1289, 2020
1392020
Kidnapped radar: Topological radar localisation using rotationally-invariant metric learning
Ş Săftescu, M Gadd, D De Martini, D Barnes, P Newman
2020 IEEE International Conference on Robotics and Automation (ICRA), 4358-4364, 2020
692020
RSS-Net: weakly-supervised multi-class semantic segmentation with FMCW radar
D De Martini, P Kaul, M Gadd, P Newman
Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV2020), 2020
65*2020
Fast radar motion estimation with a learnt focus of attention using weak supervision
R Aldera, D De Martini, M Gadd, P Newman
2019 International Conference on Robotics and Automation (ICRA), 1190-1196, 2019
632019
Rsl-net: Localising in satellite images from a radar on the ground
TY Tang, D De Martini, D Barnes, P Newman
IEEE Robotics and Automation Letters 5 (2), 1087-1094, 2020
622020
What Could Go Wrong? Introspective Radar Odometry in Challenging Environments
R Aldera, D De Martini, M Gadd, P Newman
IEEE Intelligent Transportation Systems Conference, 2019
522019
Look around you: Sequence-based radar place recognition with learned rotational invariance
M Gadd, D De Martini, P Newman
2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), 270-276, 2020
462020
Sampling, communication, and prediction co-design for synchronizing the real-world device and digital model in metaverse
Z Meng, C She, G Zhao, D De Martini
IEEE Journal on Selected Areas in Communications 41 (1), 288-300, 2022
442022
KRadar++: Coarse-to-fine FMCW scanning radar localisation
D De Martini, M Gadd, P Newman
Sensors 20 (21), 6002, 2020
282020
Self-supervised localisation between range sensors and overhead imagery
TY Tang, D De Martini, S Wu, P Newman
arXiv preprint arXiv:2006.02108, 2020
252020
Fall detection with supervised machine learning using wearable sensors
D Giuffrida, G Benetti, D De Martini, T Facchinetti
2019 IEEE 17th International Conference on Industrial Informatics (INDIN) 1 …, 2019
242019
Contrastive learning for unsupervised radar place recognition
M Gadd, D De Martini, P Newman
2021 20th International Conference on Advanced Robotics (ICAR), 344-349, 2021
232021
Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
D De Martini, M Marchegiani, P Newman, M Gadd, L Kunze
2020 IEEE Intelligent Vehicles Symposium (IV), 2020
212020
Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization
TY Tang, D De Martini, S Wu, P Newman
The International Journal of Robotics Research 40 (12-14), 1488-1509, 2021
202021
Get to the Point: Learning Lidar Place Recognition and Metric Localisation Using Overhead Imagery
TY Tang, D De Martini, P Newman
Robotics: science and systems, 2021
202021
A comparison of rssi filtering techniques for range-based localization
MA Koledoye, D De Martini, S Rigoni, T Facchinetti
2018 IEEE 23rd International Conference on Emerging Technologies and Factory …, 2018
202018
Rainbench: Towards data-driven global precipitation forecasting from satellite imagery
CS de Witt, C Tong, V Zantedeschi, D De Martini, A Kalaitzis, M Chantry, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 14902 …, 2021
19*2021
Keep off the grass: Permissible driving routes from radar with weak audio supervision
D Williams, D De Martini, M Gadd, L Marchegiani, P Newman
2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020
182020
Fast-MbyM: Leveraging translational invariance of the Fourier transform for efficient and accurate radar odometry
R Weston, M Gadd, D De Martini, P Newman, I Posner
2022 International Conference on Robotics and Automation (ICRA), 2186-2192, 2022
172022
On the road: Route proposal from radar self-supervised by fuzzy LiDAR traversability
M Broome, M Gadd, D De Martini, P Newman
AI 1 (4), 558-585, 2020
172020
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Articles 1–20