Showing 35 publications by Daniele De Martini
Point-based metric and topological localisation between lidar and overhead imagery
Tang TY, De Martini D & Newman P (2023), AUTONOMOUS ROBOTS
BibTeX
@article{pointbasedmetri-2023/1,
title={Point-based metric and topological localisation between lidar and overhead imagery},
author={Tang TY, De Martini D & Newman P},
journal={AUTONOMOUS ROBOTS},
year = "2023"
}
Visuo-tactile recognition of partial point clouds using PointNet and curriculum learning
Parsons C, Albini A, De Martini D & Maiolino P (2022), IEEE Robotics and Automation magazine
BibTeX
@article{visuotactilerec-2022/10,
title={Visuo-tactile recognition of partial point clouds using PointNet and curriculum learning},
author={Parsons C, Albini A, De Martini D & Maiolino P},
journal={IEEE Robotics and Automation magazine},
publisher={IEEE},
year = "2022"
}
RaVÆn: unsupervised change detection of extreme events using ML on-board satellites.
Růžička V, Vaughan A, De Martini D, Fulton J, Salvatelli V et al. (2022), Scientific reports, 12(1), 16939
BibTeX
@article{ravnunsupervise-2022/10,
title={RaVÆn: unsupervised change detection of extreme events using ML on-board satellites.},
author={Růžička V, Vaughan A, De Martini D, Fulton J, Salvatelli V et al.},
journal={Scientific reports},
volume={12},
number={16939},
pages={16939},
publisher={Springer Science and Business Media LLC},
year = "2022"
}
Fast-MbyM: leveraging translational invariance of the fourier transform for efficient and accurate radar odometry
Weston R, Gadd M, De Martini D, Newman P & Posner H (2022), Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2022), 2186-2192
BibTeX
@inproceedings{fastmbymleverag-2022/7,
title={Fast-MbyM: leveraging translational invariance of the fourier transform for efficient and accurate radar odometry},
author={Weston R, Gadd M, De Martini D, Newman P & Posner H},
booktitle={IEEE International Conference on Robotics and Automation (ICRA 2022)},
pages={2186-2192},
year = "2022"
}
Depth-SIMS: semi-parametric image and depth synthesis
Musat V, De Martini D, Gadd M & Newman P (2022), 2022 International Conference on Robotics and Automation (ICRA), 2388-2394
BibTeX
@inproceedings{depthsimssemipa-2022/7,
title={Depth-SIMS: semi-parametric image and depth synthesis},
author={Musat V, De Martini D, Gadd M & Newman P},
booktitle={ International Conference on Robotics and Automation (ICRA 2022)},
pages={2388-2394},
year = "2022"
}
What goes around: leveraging a constant-curvature motion constraint in radar odometry
Aldera R, Gadd M, De Martini D & Newman P (2022), IEEE Robotics and Automation Letters, 7(3), 7865-7872
BibTeX
@article{whatgoesaroundl-2022/6,
title={What goes around: leveraging a constant-curvature motion constraint in radar odometry},
author={Aldera R, Gadd M, De Martini D & Newman P},
journal={IEEE Robotics and Automation Letters},
volume={7},
pages={7865-7872},
publisher={IEEE},
year = "2022"
}
The Oxford Road Boundaries Dataset
Suleymanov T, Gadd M, De Martini D & Newman P (2022)
BibTeX
@inproceedings{theoxfordroadbo-2022/1,
title={The Oxford Road Boundaries Dataset},
author={Suleymanov T, Gadd M, De Martini D & Newman P},
booktitle={32nd IEEE Intelligent Vehicles Symposium (IV21) -- Workshop on 3D-Deep Learning for Automated Driving (3D-DLAD)},
year = "2022"
}
Contrastive learning for unsupervised radar place recognition
Gadd M, De Martini D & Newman P (2022), 2021 20th International Conference on Advanced Robotics (ICAR), 344-349
BibTeX
@inproceedings{contrastivelear-2022/1,
title={Contrastive learning for unsupervised radar place recognition},
author={Gadd M, De Martini D & Newman P},
booktitle={20th International Conference on Advanced Robotics (ICAR 2021)},
pages={344-349},
year = "2022"
}
Unsupervised change detection of extreme events using ML on-board
Ruzicka V, Vaughan A, De Martini D, Fulton J, Salvatelli V et al. (2021)
BibTeX
@inproceedings{unsupervisedcha-2021/12,
title={Unsupervised change detection of extreme events using ML on-board},
author={Ruzicka V, Vaughan A, De Martini D, Fulton J, Salvatelli V et al.},
booktitle={NeurIPS Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop (AI+HADR), 2021},
year = "2021"
}
BoxGraph: semantic place recognition and pose estimation from 3D LiDAR
Pramatarov G, De Martini D, Gadd M & Newman P (2021), Proceedings of IEEE International Conference on Intelligent Robots and Systems, 7004-7011
BibTeX
@inproceedings{boxgraphsemanti-2021/12,
title={BoxGraph: semantic place recognition and pose estimation from 3D LiDAR},
author={Pramatarov G, De Martini D, Gadd M & Newman P},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages={7004-7011},
year = "2021"
}
Sampling, communication, and prediction co-design for synchronizing the real-world device and digital model in metaverse
Meng Z, She C, Zhao G & De Martini D (2021), IEEE Journal on Selected Areas in Communications, 41(1), 288-300
BibTeX
@article{samplingcommuni-2021/11,
title={Sampling, communication, and prediction co-design for synchronizing the real-world device and digital model in metaverse
},
author={Meng Z, She C, Zhao G & De Martini D},
journal={IEEE Journal on Selected Areas in Communications},
volume={41},
pages={288-300},
publisher={IEEE},
year = "2021"
}
Fool me once: robust selective segmentation via out-of-distribution detection with contrastive learning
Williams D, Gadd M, De Martini D & Newman P (2021), 2021 IEEE International Conference on Robotics and Automation (ICRA), 9536-9542
BibTeX
@inproceedings{foolmeoncerobus-2021/10,
title={Fool me once: robust selective segmentation via out-of-distribution detection with contrastive learning},
author={Williams D, Gadd M, De Martini D & Newman P},
booktitle={ICRA 2021},
pages={9536-9542},
year = "2021"
}
Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization
Tang TY, De Martini D, Wu S & Newman P (2021), The International Journal of Robotics Research, 40(12-14), 1488-1509
BibTeX
@article{selfsupervisedl-2021/9,
title={Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization},
author={Tang TY, De Martini D, Wu S & Newman P},
journal={The International Journal of Robotics Research},
volume={40},
pages={1488-1509},
publisher={SAGE Publications},
year = "2021"
}
Get to the point: learning lidar place recognition and metric localisation using overhead imagery
Tang TY, De Martini D & Newman P (2021), Proceedings of Robotics: Science and Systems, 2021
BibTeX
@article{gettothepointle-2021/7,
title={Get to the point: learning lidar place recognition and metric localisation using overhead imagery},
author={Tang TY, De Martini D & Newman P},
journal={Proceedings of Robotics: Science and Systems, 2021},
publisher={Robotics: Science and Systems},
year = "2021"
}
Unsupervised place recognition with deep embedding learning over radar videos
Gadd M, De Martini D & Newman P (2021)
BibTeX
@inproceedings{unsupervisedpla-2021/5,
title={Unsupervised place recognition with deep embedding learning over radar videos},
author={Gadd M, De Martini D & Newman P},
booktitle={2021 ICRA Workshop: Radar Perception for All-Weather Autonomy},
year = "2021"
}
RainBench: Towards global precipitation forecasting from satellite imagery
de Witt CS, Tong C, Zantedeschi V, De Martini D, Kalaitzis A et al. (2021), Thirty-fifth AAAI Conference on Artificial Intelligence, 35(17), 14902-14910
BibTeX
@inproceedings{rainbenchtoward-2021/5,
title={RainBench: Towards global precipitation forecasting from satellite imagery},
author={de Witt CS, Tong C, Zantedeschi V, De Martini D, Kalaitzis A et al.},
booktitle={35th AAAI Conference on Artificial Intelligence (AAAI 2021)},
pages={14902-14910},
year = "2021"
}
The hulk: design and development of a weather-proof vehicle for long-term autonomy in outdoor environments
Kyberd S, Attias J, Get P, Murcutt P, Prahacs C et al. (2021), International Conference on Field and Service Robotics (FSR), 16(2021), 101-114
BibTeX
@inproceedings{thehulkdesignan-2021/1,
title={The hulk: design and development of a weather-proof vehicle for long-term autonomy in outdoor environments},
author={Kyberd S, Attias J, Get P, Murcutt P, Prahacs C et al.},
pages={101-114},
year = "2021"
}
RSS-Net: weakly-supervised multi-class semantic segmentation with FMCW radar
Kaul P, De Martini D, Gadd M & Newman P (2021), Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV2020)(2020), 431-436
BibTeX
@inproceedings{rssnetweaklysup-2021/1,
title={RSS-Net: weakly-supervised multi-class semantic segmentation with FMCW radar},
author={Kaul P, De Martini D, Gadd M & Newman P},
booktitle={IEEE Intelligent Vehicles Symposium (IV)},
pages={431-436},
year = "2021"
}
Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
Gadd M, De Martini D, Marchegiani M, Newman P & Kunze L (2021), Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), 150-155
BibTeX
@inproceedings{senseassessexpl-2021/1,
title={Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios},
author={Gadd M, De Martini D, Marchegiani M, Newman P & Kunze L},
booktitle={IEEE Intelligent Vehicles Symposium (IV), Workshop on Ensuring and Validating Safety for Automated Vehicles (EVSAV)},
pages={150-155},
year = "2021"
}
CPG-Actor: Reinforcement Learning for Central Pattern Generators
Campanaro L, Gangapurwala S, De Martini D, Merkt W & Havoutis I (2021), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13054 LNAI, 25-35
BibTeX
@inproceedings{cpgactorreinfor-2021/1,
title={CPG-Actor: Reinforcement Learning for Central Pattern Generators},
author={Campanaro L, Gangapurwala S, De Martini D, Merkt W & Havoutis I},
pages={25-35},
year = "2021"
}
Online Fall Detection Using Recurrent Neural Networks on Smart Wearable Devices
Musci M, De Martini D, Blago N, Facchinetti T & Piastra M (2021), IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 9(3), 1276-1289
BibTeX
@article{onlinefalldetec-2021/,
title={Online Fall Detection Using Recurrent Neural Networks on Smart Wearable Devices},
author={Musci M, De Martini D, Blago N, Facchinetti T & Piastra M},
journal={IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING},
volume={9},
pages={1276-1289},
year = "2021"
}
Keep off the grass: permissible driving routes from radar with weak audio supervision
Williams D, De Martini D, Gadd M, Marchegiani M & Newman P (2020), Proceedings of the 2020 IEEE Intelligent Transportation Systems Conference (ITSC), 1-6
BibTeX
@inproceedings{keepoffthegrass-2020/12,
title={Keep off the grass: permissible driving routes from radar with weak audio supervision},
author={Williams D, De Martini D, Gadd M, Marchegiani M & Newman P},
booktitle={IEEE Intelligent Transportation Systems Conference (ITSC)},
pages={1-6},
year = "2020"
}
On the road: route proposal from radar self-supervised by fuzzy LiDAR traversability
Broome M, Gadd M, De Martini D & Newman P (2020), AI, 1(4), 558-585
BibTeX
@article{ontheroadroutep-2020/12,
title={On the road: route proposal from radar self-supervised by fuzzy LiDAR traversability},
author={Broome M, Gadd M, De Martini D & Newman P},
journal={AI},
volume={1},
pages={558-585},
publisher={MDPI},
year = "2020"
}
kRadar++: coarse-to-fine FMCW scanning radar localisation
De Martini D, Gadd M & Newman P (2020), Sensors, 20(21)
BibTeX
@article{kradarcoarsetof-2020/10,
title={kRadar++: coarse-to-fine FMCW scanning radar localisation},
author={De Martini D, Gadd M & Newman P},
journal={Sensors},
volume={20},
number={6002},
publisher={MDPI},
year = "2020"
}
Fault Detection of Electromechanical Actuators via Automatic Generation of a Fuzzy Index
De Martini D & Facchinetti T (2020), IEEE-ASME TRANSACTIONS ON MECHATRONICS, 25(5), 2197-2207
BibTeX
@article{faultdetectiono-2020/10,
title={Fault Detection of Electromechanical Actuators via Automatic Generation of a Fuzzy Index},
author={De Martini D & Facchinetti T},
journal={IEEE-ASME TRANSACTIONS ON MECHATRONICS},
volume={25},
pages={2197-2207},
year = "2020"
}
Kidnapped radar: topological radar localisation using rotationally-invariant metric learning
GADD M, Schrodl , DE MARTINI D, Barnes D & NEWMAN P (2020), Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA)(2020), 4358-4364
BibTeX
@inproceedings{kidnappedradart-2020/9,
title={Kidnapped radar: topological radar localisation using rotationally-invariant metric learning},
author={GADD M, Schrodl , DE MARTINI D, Barnes D & NEWMAN P},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
pages={4358-4364},
year = "2020"
}
Self-supervised localisation between range sensors and overhead imagery
Tang TY, De Martini D, Wu S & Newman P (2020), Proceedings of Robotics: Science and Systems XVI(2020)
BibTeX
@inproceedings{selfsupervisedl-2020/7,
title={Self-supervised localisation between range sensors and overhead imagery},
author={Tang TY, De Martini D, Wu S & Newman P},
year = "2020"
}
Look around you: sequence-based radar place recognition with learned rotational invariance
Gadd M, De Martini D & Newman P (2020), Position Location and Navigation (PLANS), IEEE Symposium
BibTeX
@inproceedings{lookaroundyouse-2020/6,
title={Look around you: sequence-based radar place recognition with learned rotational invariance},
author={Gadd M, De Martini D & Newman P},
booktitle={IEEE/ION Position, Location and Navigation Symposium (PLANS) 2020},
year = "2020"
}
RSL-Net: localising in satellite images from a radar on the ground
Tang TY, De Martini D, Barnes D & Newman P (2020), IEEE Robotics and Automation Letters, 5(2), 1087-1094
BibTeX
@article{rslnetlocalisin-2020/4,
title={RSL-Net: localising in satellite images from a radar on the ground},
author={Tang TY, De Martini D, Barnes D & Newman P},
journal={IEEE Robotics and Automation Letters},
volume={5},
pages={1087-1094},
publisher={IEEE},
year = "2020"
}
Distributed architecture for a smart LEDs display system based on MQTT
Facchinetti T, Onandin AB, Enetti GB, De Martini D & IEEE (2020), 2020 25TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 1239-1242
BibTeX
@inproceedings{distributedarch-2020/,
title={Distributed architecture for a smart LEDs display system based on MQTT},
author={Facchinetti T, Onandin AB, Enetti GB, De Martini D & IEEE },
pages={1239-1242},
year = "2020"
}
What could go wrong? Introspective radar odometry in challenging environments
Aldera R, De Martini D, Gadd M & Newman P (2019), 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2835-2842
BibTeX
@inproceedings{whatcouldgowron-2019/11,
title={What could go wrong? Introspective radar odometry in challenging environments},
author={Aldera R, De Martini D, Gadd M & Newman P},
pages={2835-2842},
year = "2019"
}
Fast radar motion estimation with a learnt focus of attention using weak supervision
Aldera R, De Martini D, Gadd M & Newman P (2019), 2019 International Conference on Robotics and Automation (ICRA), 1190-1196
BibTeX
@inproceedings{fastradarmotion-2019/8,
title={Fast radar motion estimation with a learnt focus of attention using weak supervision},
author={Aldera R, De Martini D, Gadd M & Newman P},
pages={1190-1196},
year = "2019"
}
No Need to Scream: Robust Sound-Based Speaker Localisation in Challenging Scenarios
Tse THE, De Martini D & Marchegiani L (2019), SOCIAL ROBOTICS, ICSR 2019, 11876, 176-185
BibTeX
@inproceedings{noneedtoscreamr-2019/,
title={No Need to Scream: Robust Sound-Based Speaker Localisation in Challenging Scenarios},
author={Tse THE, De Martini D & Marchegiani L},
pages={176-185},
year = "2019"
}
Fall Detection with Supervised Machine Learning using Wearable Sensors
Giuffrida D, Benetti G, De Martini D, Facchinetti T & IEEE (2019), 2019 IEEE 17TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 253-259
BibTeX
@inproceedings{falldetectionwi-2019/,
title={Fall Detection with Supervised Machine Learning using Wearable Sensors},
author={Giuffrida D, Benetti G, De Martini D, Facchinetti T & IEEE },
pages={253-259},
year = "2019"
}
Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations
Ramtoula B, Gadd M, Newman P & De Martini D (0)
BibTeX
@inproceedings{visualdnarepres-/,
title={Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations},
author={Ramtoula B, Gadd M, Newman P & De Martini D},
booktitle={Conference on Computer Vision and Pattern Recognition}
}