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ORCIDDaniele De Martini
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2020 – today
- 2024
[j17]Daniele De Martini
, Guido Benetti, Tullio Facchinetti:
Real-time cyber/physical interplay in scheduling for peak load optimisation in Cyber-Physical Energy Systems. Intell. Syst. Appl. 22: 200380 (2024)- [j16]Valentina Musat, Daniele De Martini, Matthew Gadd, Paul Newman:
NeuralFloors: Conditional Street-Level Scene Generation From BEV Semantic Maps via Neural Fields. IEEE Robotics Autom. Lett. 9(3): 2431-2438 (2024) - [j15]David S. W. Williams, Daniele De Martini, Matthew Gadd, Paul Newman:
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation From Unlabeled Data. IEEE Trans. Robotics 40: 3146-3165 (2024) - [c39]Georgi Pramatarov, Matthew Gadd, Paul Newman, Daniele De Martini:
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation. ICRA 2024: 12276-12282 - [c38]Benjamin Ramtoula, Daniele De Martini, Matthew Gadd, Paul Newman:
VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition. ICRA 2024: 15883-15889 - [c37]David S. W. Williams, Matthew Gadd, Paul Newman, Daniele De Martini:
Masked γ-SSL: Learning Uncertainty Estimation via Masked Image Modeling. ICRA 2024: 16192-16198 - [c36]Yufeng Diao, Yichi Zhang, Philip G. Zhao, Daniele De Martini:
TAGIC: Task-Guided Image Communication Framework for Seamless Teleoperation. INFOCOM (Workshops) 2024: 1-2 - [c35]Efimia Panagiotaki, Tyler Reinmund, Stephan Mouton, Luke Pitt, Arundathi Shaji Shanthini, Wayne Tubby, Matthew Towlson, Samuel Sze, Brian Liu, Chris Prahacs, Daniele De Martini, Lars Kunze:
RobotCycle: Assessing Cycling Safety in Urban Environments. IV 2024: 357-363 - [i37]David S. W. Williams, Matthew Gadd, Paul Newman, Daniele De Martini:
Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling. CoRR abs/2402.17622 (2024) - [i36]David S. W. Williams, Daniele De Martini, Matthew Gadd, Paul Newman:
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data. CoRR abs/2402.17653 (2024) - [i35]Matthew Gadd, Daniele De Martini, Oliver Bartlett, Paul Murcutt, Matthew Towlson, Matthew Widojo, Valentina Musat, Luke Robinson, Efimia Panagiotaki, Georgi Pramatarov, Marc Alexander Kühn, Letizia Marchegiani, Paul Newman, Lars Kunze:
OORD: The Oxford Offroad Radar Dataset. CoRR abs/2403.02845 (2024) - [i34]Georgi Pramatarov, Matthew Gadd, Paul Newman, Daniele De Martini:
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation. CoRR abs/2403.04755 (2024) - [i33]Efimia Panagiotaki, Tyler Reinmund, Brian Liu, Stephan Mouton, Luke Pitt, Arundathi Shaji Shanthini, Matthew Towlson, Wayne Tubby, Chris Prahacs, Daniele De Martini, Lars Kunze:
RobotCycle: Assessing Cycling Safety in Urban Environments. CoRR abs/2403.07789 (2024) - [i32]Benjamin Ramtoula, Daniele De Martini, Matthew Gadd, Paul M. Newman:
VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition. CoRR abs/2403.09025 (2024) - [i31]Matthew Gadd, Daniele De Martini, Luke Pitt, Wayne Tubby, Matthew Towlson, Chris Prahacs, Oliver Bartlett, John Jackson, Man Qi, Paul Newman, Andrew Hector, Roberto Salguero-Gómez, Nick Hawes:
Watching Grass Grow: Long-term Visual Navigation and Mission Planning for Autonomous Biodiversity Monitoring. CoRR abs/2404.10446 (2024) - [i30]Daoxin Zhong, Luke Robinson, Daniele De Martini:
NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing. CoRR abs/2408.01251 (2024) - 2023
- [j14]Tim Yuqing Tang, Daniele De Martini, Paul Newman:
Point-based metric and topological localisation between lidar and overhead imagery. Auton. Robots 47(5): 595-615 (2023) - [j13]Zhen Meng, Changyang She, Guodong Zhao, Daniele De Martini:
Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse. IEEE J. Sel. Areas Commun. 41(1): 288-300 (2023) - [j12]Christopher Parsons, Alessandro Albini, Daniele De Martini, Perla Maiolino:
Visuo-Tactile Recognition of Partial Point Clouds Using PointNet and Curriculum Learning: Enabling Tactile Perception from Visual Data. IEEE Robotics Autom. Mag. 30(3): 69-78 (2023) - [c34]Benjamin Ramtoula, Matthew Gadd, Paul Newman, Daniele De Martini:
Visual DNA: Representing and Comparing Images Using Distributions of Neuron Activations. CVPR 2023: 11113-11123 - [c33]Efimia Panagiotaki, Daniele De Martini, Georgi Pramatarov, Matthew Gadd, Lars Kunze:
SEM-GAT: Explainable Semantic Pose Estimation Using Learned Graph Attention. ICAR 2023: 367-374 - [c32]Efimia Panagiotaki, Daniele De Martini, Lars Kunze:
Semantic Interpretation and Validation of Graph Attention-Based Explanations for GNN Models. ICAR 2023: 375-380 - [c31]Pawit Kochakarn, Daniele De Martini, Daniel Omeiza, Lars Kunze:
Explainable Action Prediction through Self-Supervision on Scene Graphs. ICRA 2023: 1479-1485 - [c30]Luke Robinson, Daniele De Martini, Matthew Gadd, Paul Newman:
Visual Servoing on Wheels: Robust Robot Orientation Estimation in Remote Viewpoint Control. IROS 2023: 6364-6370 - [c29]Fraser Rennie, David S. W. Williams, Paul Newman, Daniele De Martini:
Doppler-Aware Odometry from FMCW Scanning Radar. ITSC 2023: 5126-5132 - [c28]Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis:
Roll-Drop: accounting for observation noise with a single parameter. L4DC 2023: 718-730 - [c27]Tim Y. Tang, Daniele De Martini, Paul M. Newman:
Self-Supervised Lidar Place Recognition in Overhead Imagery Using Unpaired Data. Robotics: Science and Systems 2023 - [i29]Pawit Kochakarn, Daniele De Martini, Daniel Omeiza, Lars Kunze:
Explainable Action Prediction through Self-Supervision on Scene Graphs. CoRR abs/2302.03477 (2023) - [i28]Benjamin Ramtoula, Matthew Gadd, Paul Newman, Daniele De Martini:
Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations. CoRR abs/2304.10036 (2023) - [i27]Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis:
Roll-Drop: accounting for observation noise with a single parameter. CoRR abs/2304.13150 (2023) - [i26]Luke Robinson, Daniele De Martini, Matthew Gadd, Paul Newman:
Visual Servoing on Wheels: Robust Robot Orientation Estimation in Remote Viewpoint Control. CoRR abs/2306.14848 (2023) - [i25]Efimia Panagiotaki, Daniele De Martini, Georgi Pramatarov, Matthew Gadd, Lars Kunze:
SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention. CoRR abs/2308.03718 (2023) - [i24]Efimia Panagiotaki, Daniele De Martini, Lars Kunze:
Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models. CoRR abs/2308.04220 (2023) - [i23]Fraser Rennie, David S. W. Williams, Paul Newman, Daniele De Martini:
Doppler-aware Odometry from FMCW Scanning Radar. CoRR abs/2308.10597 (2023) - [i22]Matthew Gadd, Benjamin Ramtoula, Daniele De Martini, Paul Newman:
What you see is what you get: Experience ranking with deep neural dataset-to-dataset similarity for topological localisation. CoRR abs/2310.13622 (2023) - [i21]Luke Robinson, Matthew Gadd, Paul Newman, Daniele De Martini:
Robot-Relay : Building-Wide, Calibration-Less Visual Servoing with Learned Sensor Handover Network. CoRR abs/2310.15677 (2023) - 2022
- [j11]Roberto Aldera, Matthew Gadd, Daniele De Martini, Paul Newman:
What Goes Around: Leveraging a Constant-Curvature Motion Constraint in Radar Odometry. IEEE Robotics Autom. Lett. 7(3): 7865-7872 (2022) - [c26]Rob Weston, Matthew Gadd, Daniele De Martini, Paul Newman, Ingmar Posner:
Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry. ICRA 2022: 2186-2192 - [c25]Valentina Musat, Daniele De Martini, Matthew Gadd, Paul Newman:
Depth-SIMS: Semi-Parametric Image and Depth Synthesis. ICRA 2022: 2388-2394 - [c24]Georgi Pramatarov, Daniele De Martini, Matthew Gadd, Paul Newman:
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR. IROS 2022: 7004-7011 - [i20]Rob Weston, Matthew Gadd, Daniele De Martini, Paul Newman, Ingmar Posner:
Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry. CoRR abs/2203.00459 (2022) - [i19]Valentina Musat, Daniele De Martini, Matthew Gadd, Paul Newman:
Depth-SIMS: Semi-Parametric Image and Depth Synthesis. CoRR abs/2203.03405 (2022) - [i18]Roberto Aldera, Matthew Gadd, Daniele De Martini, Paul Newman:
What Goes Around: Leveraging a Constant-curvature Motion Constraint in Radar Odometry. CoRR abs/2206.10517 (2022) - [i17]Georgi Pramatarov, Daniele De Martini, Matthew Gadd, Paul Newman:
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR. CoRR abs/2206.15154 (2022) - [i16]Zhen Meng, Changyang She, Guodong Zhao, Daniele De Martini:
Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse. CoRR abs/2208.04233 (2022) - 2021
- [j10]Tim Y. Tang, Daniele De Martini, Shangzhe Wu, Paul Newman:
Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization. Int. J. Robotics Res. 40(12-14): 1488-1509 (2021) - [j9]Mirto Musci, Daniele De Martini, Nicola Blago, Tullio Facchinetti, Marco Piastra:
Online Fall Detection Using Recurrent Neural Networks on Smart Wearable Devices. IEEE Trans. Emerg. Top. Comput. 9(3): 1276-1289 (2021) - [c23]Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Alfredo Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski:
RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery. AAAI 2021: 14902-14910 - [c22]Matthew Gadd, Daniele De Martini, Paul Newman:
Contrastive Learning for Unsupervised Radar Place Recognition. ICAR 2021: 344-349 - [c21]David S. W. Williams, Matthew Gadd, Daniele De Martini, Paul M. Newman:
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning. ICRA 2021: 9536-9542 - [c20]Tarlan Suleymanov, Matthew Gadd, Daniele De Martini, Paul Newman:
The Oxford Road Boundaries Dataset. IV Workshops 2021: 222-227 - [c19]Tim Y. Tang, Daniele De Martini, Paul Newman:
Get to the Point: Learning Lidar Place Recognition and Metric Localisation Using Overhead Imagery. Robotics: Science and Systems 2021 - [c18]Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini, Wolfgang Merkt, Ioannis Havoutis:
CPG-Actor: Reinforcement Learning for Central Pattern Generators. TAROS 2021: 25-35 - [i15]Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini, Wolfgang Merkt, Ioannis Havoutis:
CPG-ACTOR: Reinforcement Learning for Central Pattern Generators. CoRR abs/2102.12891 (2021) - [i14]David S. W. Williams, Matthew Gadd, Daniele De Martini, Paul Newman:
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning. CoRR abs/2103.00869 (2021) - [i13]Matthew Gadd, Daniele De Martini, Paul Newman:
Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos. CoRR abs/2106.06703 (2021) - [i12]Tarlan Suleymanov, Matthew Gadd, Daniele De Martini, Paul Newman:
The Oxford Road Boundaries Dataset. CoRR abs/2106.08983 (2021) - [i11]Matthew Gadd, Daniele De Martini, Paul Newman:
Contrastive Learning for Unsupervised Radar Place Recognition. CoRR abs/2110.02744 (2021) - [i10]Vít Ruzicka, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi:
Unsupervised Change Detection of Extreme Events Using ML On-Board. CoRR abs/2111.02995 (2021) - 2020
- [j8]Tim Yuqing Tang, Daniele De Martini, Dan Barnes, Paul Newman:
RSL-Net: Localising in Satellite Images From a Radar on the Ground. IEEE Robotics Autom. Lett. 5(2): 1087-1094 (2020) - [j7]Daniele De Martini, Matthew Gadd, Paul Newman:
kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation. Sensors 20(21): 6002 (2020) - [c17]Tullio Facchinetti, Andrea Bonandin, Guido Benetti, Daniele De Martini:
Distributed architecture for a smart LEDs display system based on MQTT. ETFA 2020: 1243-1246 - [c16]Stefan Saftescu, Matthew Gadd, Daniele De Martini, Dan Barnes, Paul Newman:
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning. ICRA 2020: 4358-4364 - [c15]David S. W. Williams, Daniele De Martini, Matthew Gadd, Letizia Marchegiani, Paul Newman:
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision. ITSC 2020: 1-6 - [c14]Matthew Gadd, Daniele De Martini, Letizia Marchegiani, Paul Newman, Lars Kunze:
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios. IV 2020: 150-155 - [c13]Prannay Kaul, Daniele De Martini, Matthew Gadd, Paul Newman:
RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar. IV 2020: 431-436 - [c12]Matthew Gadd, Daniele De Martini, Paul Newman:
Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance. PLANS 2020: 270-276 - [c11]Tim Y. Tang, Daniele De Martini, Shangzhe Wu, Paul Newman:
Self-Supervised Localisation between Range Sensors and Overhead Imagery. Robotics: Science and Systems 2020 - [i9]Tim Y. Tang, Daniele De Martini, Dan Barnes, Paul Newman:
RSL-Net: Localising in Satellite Images From a Radar on the Ground. CoRR abs/2001.03233 (2020) - [i8]Stefan Saftescu, Matthew Gadd, Daniele De Martini, Dan Barnes, Paul Newman:
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning. CoRR abs/2001.09438 (2020) - [i7]Matthew Gadd, Daniele De Martini, Paul Newman:
Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance. CoRR abs/2003.04699 (2020) - [i6]Prannay Kaul, Daniele De Martini, Matthew Gadd, Paul Newman:
RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar. CoRR abs/2004.03451 (2020) - [i5]Matthew Gadd, Daniele De Martini, Letizia Marchegiani, Paul Newman, Lars Kunze:
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios. CoRR abs/2005.02031 (2020) - [i4]David S. W. Williams, Daniele De Martini, Matthew Gadd, Letizia Marchegiani, Paul Newman:
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision. CoRR abs/2005.05175 (2020) - [i3]Tim Y. Tang, Daniele De Martini, Shangzhe Wu, Paul Newman:
Self-Supervised Localisation between Range Sensors and Overhead Imagery. CoRR abs/2006.02108 (2020) - [i2]Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski:
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery. CoRR abs/2012.09670 (2020)
2010 – 2019
- 2019
- [c10]Stephen Kyberd, Jonathan Attias, Peter Get, Paul Murcutt, Chris Prahacs, Matthew Towlson, Simon Venn, Andreia Vasconcelos, Matthew Gadd, Daniele De Martini, Paul Newman:
The Hulk: Design and Development of a Weather-Proof Vehicle for Long-Term Autonomy in Outdoor Environments. FSR 2019: 101-114 - [c9]Roberto Aldera, Daniele De Martini, Matthew Gadd, Paul Newman:
Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision. ICRA 2019: 1190-1196 - [c8]Davide Giuffrida, Guido Benetti, Daniele De Martini, Tullio Facchinetti:
Fall Detection with Supervised Machine Learning using Wearable Sensors. INDIN 2019: 253-259 - [c7]Roberto Aldera, Daniele De Martini, Matthew Gadd, Paul Newman:
What Could Go Wrong? Introspective Radar Odometry in Challenging Environments. ITSC 2019: 2835-2842 - [c6]Tze Ho Elden Tse, Daniele De Martini, Letizia Marchegiani:
No Need to Scream: Robust Sound-Based Speaker Localisation in Challenging Scenarios. ICSR 2019: 176-185 - 2018
- [c5]Moses A. Koledoye, Daniele De Martini, Simone Rigoni, Tullio Facchinetti:
A Comparison of RSSI Filtering Techniques for Range-based Localization. ETFA 2018: 761-767 - [c4]Daniele De Martini, Guido Benetti, Tullio Facchinetti:
Cyber/physical interplay in the real-time scheduling for peak load optimization of electric loads. ICPS 2018: 97-102 - [i1]Mirto Musci, Daniele De Martini, Nicola Blago, Tullio Facchinetti, Marco Piastra:
Online Fall Detection using Recurrent Neural Networks. CoRR abs/1804.04976 (2018) - 2017
- [j6]Moses A. Koledoye, Daniele De Martini, Massimo Carvani, Tullio Facchinetti:
Design of a Mobile Robot for Air Ducts Exploration. Robotics 6(4): 26 (2017) - [j5]Daniele De Martini, Guido Benetti, Marco L. Della Vedova, Tullio Facchinetti:
Adaptive Real-Time Scheduling of Cyber-Physical Energy Systems. ACM Trans. Cyber Phys. Syst. 1(4): 20:1-20:25 (2017) - [j4]Daniele De Martini, Giuseppe Valerio Gramazio, Alessandro Bertini, Carlo Rottenbacher, Tullio Facchinetti:
Design and Modeling of a Quadcopter with Double Axis Tilting Rotors. Unmanned Syst. 5(3): 169-180 (2017) - [c3]Daniele De Martini, Guido Benetti, Filippo Cipolla, Davide Caprino, Marco L. Della Vedova, Tullio Facchinetti:
Peak load optimization through 2-dimensional packing and multi-processor real-time scheduling. Conf. Computing Frontiers 2017: 275-278 - [c2]Daniele De Martini, Gianluca Roveda, Alessandro Bertini, Agnese Marchini, Tullio Facchinetti:
A Framework for Automatic Generation of Fuzzy Evaluation Systems for Embedded Applications. IJCCI 2017: 281-288 - [c1]Daniele De Martini, Andrea Bonandin, Tullio Facchinetti:
eduMorse: An Open-Source Framework for Mobile Robotics Education. RiE 2017: 289-300 - 2016
- [j3]Lucio De Capitani, Daniele De Martini:
Reproducibility Probability Estimation and RP-Testing for Some Nonparametric Tests. Entropy 18(4): 142 (2016) - 2011
- [j2]Daniele De Martini:
Robustness and Corrections for Sample Size Adaptation Strategies Based on Effect Size Estimation. Commun. Stat. Simul. Comput. 40(9): 1263-1277 (2011)
2000 – 2009
- 2008
- [j1]Daniele De Martini:
Evaluating the Risk in Sample Size Determination. Commun. Stat. Simul. Comput. 37(9): 1776-1784 (2008)
Coauthor Index
Paul Newman 0001
aka: Paul M. Newman
aka: Paul M. Newman
[j16] [j15] [c39] [c38] [c37] [i37] [i36] [i35] [i34] [i32] [i31] [j14] [c34] [c30] [c29] [c27] [i28] [i26] [i23] [i22] [i21] [j11] [c26] [c25] [c24] [i20] [i19] [i18] [i17] [j10] [c22] [c21] [c20] [c19] [i14] [i13] [i12] [i11] [j8] [j7] [c16] [c15] [c14] [c13] [c12] [c11] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [c10] [c9] [c7]
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