Matthew Gadd

1.0k total citations · 1 hit paper
28 papers, 583 citations indexed

About

Matthew Gadd is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Matthew Gadd has authored 28 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 16 papers in Aerospace Engineering and 8 papers in Artificial Intelligence. Recurrent topics in Matthew Gadd's work include Robotics and Sensor-Based Localization (15 papers), Indoor and Outdoor Localization Technologies (5 papers) and Advanced Neural Network Applications (5 papers). Matthew Gadd is often cited by papers focused on Robotics and Sensor-Based Localization (15 papers), Indoor and Outdoor Localization Technologies (5 papers) and Advanced Neural Network Applications (5 papers). Matthew Gadd collaborates with scholars based in United Kingdom, United States and China. Matthew Gadd's co-authors include Paul Newman, Ingmar Posner, Paul Murcutt, Dan Barnes, Daniele De Martini, P. R. Newman, Paul A. Newman, Lars Kunze, Michael Broomé and Shuyang Sun and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Matthew Gadd

25 papers receiving 563 citations

Hit Papers

The Oxford Radar RobotCar Dataset: A Radar Extension to t... 2020 2026 2022 2024 2020 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
    2020 280 citations Dan Barnes, Matthew Gadd et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matthew Gadd United Kingdom 12 399 236 183 99 94 28 583
Damien Vivet France 10 342 0.9× 239 1.0× 146 0.8× 72 0.7× 71 0.8× 37 550
Renaud Dubé Switzerland 14 469 1.2× 385 1.6× 116 0.6× 109 1.1× 80 0.9× 20 675
Gon-Woo Kim South Korea 11 364 0.9× 343 1.5× 119 0.7× 94 0.9× 42 0.4× 61 575
Kiyosumi Kidono Japan 13 156 0.4× 310 1.3× 108 0.6× 81 0.8× 45 0.5× 33 527
Dominik Kellner Germany 12 463 1.2× 119 0.5× 174 1.0× 45 0.5× 241 2.6× 17 630
Ryan W. Wolcott United States 5 439 1.1× 312 1.3× 143 0.8× 178 1.8× 30 0.3× 9 597
Huan Yin China 15 486 1.2× 326 1.4× 204 1.1× 159 1.6× 46 0.5× 41 655
Young-Sik Shin South Korea 12 543 1.4× 448 1.9× 169 0.9× 122 1.2× 45 0.5× 32 763
Alireza Asvadi Portugal 10 215 0.5× 380 1.6× 57 0.3× 184 1.9× 56 0.6× 18 631
Li Tang China 16 388 1.0× 433 1.8× 112 0.6× 137 1.4× 36 0.4× 44 822

Countries citing papers authored by Matthew Gadd

Since Specialization
Citations

This map shows the geographic impact of Matthew Gadd's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew Gadd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Gadd more than expected).

Fields of papers citing papers by Matthew Gadd

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew Gadd. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew Gadd. The network helps show where Matthew Gadd may publish in the future.

Co-authorship network of co-authors of Matthew Gadd

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Gadd. A scholar is included among the top collaborators of Matthew Gadd based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthew Gadd. Matthew Gadd is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Qi, Man, Matthew Gadd, Daniele De Martini, et al.. (2025). Biodiversity research requires more motors in air, water and on land. Methods in Ecology and Evolution. 17(3). 668–682.
2.
Niu, Hanlin, Matthew Gadd, Andrejs Schütz, et al.. (2025). AutoInspect: Toward Long-Term Autonomous Inspection and Monitoring. 2. 529–548.
3.
Gadd, Matthew, Daniele De Martini, Paul Murcutt, et al.. (2024). OORD: The Oxford Offroad Radar Dataset. IEEE Transactions on Intelligent Transportation Systems. 25(11). 18779–18790. 9 indexed citations
4.
Gadd, Matthew, et al.. (2024). That’s My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation. 12276–12282. 1 indexed citations
5.
Williams, David, Matthew Gadd, Paul Newman, & Daniele De Martini. (2024). Masked γ-SSL: Learning Uncertainty Estimation via Masked Image Modeling. 16192–16198. 1 indexed citations
6.
7.
Martini, Daniele De, et al.. (2024). VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition. 30. 15883–15889. 1 indexed citations
8.
Gadd, Matthew, et al.. (2023). Visual DNA: Representing and Comparing Images Using Distributions of Neuron Activations. 11113–11123. 3 indexed citations
9.
Martini, Daniele De, et al.. (2022). BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 7004–7011. 18 indexed citations
10.
Gadd, Matthew, et al.. (2022). Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry. 2022 International Conference on Robotics and Automation (ICRA). 2186–2192. 21 indexed citations
11.
Gadd, Matthew, et al.. (2022). What Goes Around: Leveraging a Constant-Curvature Motion Constraint in Radar Odometry. IEEE Robotics and Automation Letters. 7(3). 7865–7872. 17 indexed citations
12.
Gadd, Matthew, et al.. (2021). Fool me once: robust selective segmentation via out-of-distribution detection with contrastive learning. Oxford University Research Archive (ORA) (University of Oxford). 5 indexed citations
13.
Gadd, Matthew, et al.. (2021). RSS-Net: weakly-supervised multi-class semantic segmentation with FMCW radar. Oxford University Research Archive (ORA) (University of Oxford). 38 indexed citations
14.
Gadd, Matthew, Daniele De Martini, & Paul Newman. (2021). Contrastive Learning for Unsupervised Radar Place Recognition. 15 indexed citations
15.
Barnes, Dan, Matthew Gadd, Paul Murcutt, Paul Newman, & Ingmar Posner. (2020). The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset. 6433–6438. 280 indexed citations breakdown →
16.
Martini, Daniele De, Matthew Gadd, & Paul Newman. (2020). kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation. Sensors. 20(21). 6002–6002. 23 indexed citations
17.
Broomé, Michael, Matthew Gadd, Daniele De Martini, & Paul Newman. (2020). On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability. SHILAP Revista de lepidopterología. 1(4). 558–585. 11 indexed citations
18.
Martini, Daniele De, et al.. (2019). Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision. 1190–1196. 48 indexed citations
19.
Martini, Daniele De, et al.. (2019). What Could Go Wrong? Introspective Radar Odometry in Challenging Environments. 2835–2842. 34 indexed citations
20.
Gadd, Matthew & Paul Newman. (2016). Checkout my map: Version control for fleetwide visual localisation. Oxford University Research Archive (ORA) (University of Oxford). 5729–5736. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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