| 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 | 139 | 2020 |
| 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 | 69 | 2020 |
| 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 | 63 | 2019 |
| 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 | 62 | 2020 |
| 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 | 52 | 2019 |
| 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 | 46 | 2020 |
| 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 | 45 | 2022 |
| KRadar++: Coarse-to-fine FMCW scanning radar localisation D De Martini, M Gadd, P Newman Sensors 20 (21), 6002, 2020 | 28 | 2020 |
| Self-supervised localisation between range sensors and overhead imagery TY Tang, D De Martini, S Wu, P Newman arXiv preprint arXiv:2006.02108, 2020 | 25 | 2020 |
| 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 | 24 | 2019 |
| 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 | 23 | 2021 |
| 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 | 21 | 2020 |
| 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 | 20 | 2021 |
| 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 | 20 | 2021 |
| 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 | 20 | 2018 |
| 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 | 18 | 2020 |
| 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 | 17 | 2022 |
| 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 | 17 | 2020 |
Paul NewmanProfessor University of OxfordEmail verificata su robots.ox.ac.uk
