Will Maddern

3.6k total citations · 1 hit paper
19 papers, 1.7k citations indexed

About

Will Maddern is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Environmental Engineering. According to data from OpenAlex, Will Maddern has authored 19 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 14 papers in Aerospace Engineering and 4 papers in Environmental Engineering. Recurrent topics in Will Maddern's work include Robotics and Sensor-Based Localization (14 papers), Advanced Image and Video Retrieval Techniques (8 papers) and Advanced Vision and Imaging (7 papers). Will Maddern is often cited by papers focused on Robotics and Sensor-Based Localization (14 papers), Advanced Image and Video Retrieval Techniques (8 papers) and Advanced Vision and Imaging (7 papers). Will Maddern collaborates with scholars based in United Kingdom, Australia and United States. Will Maddern's co-authors include Paul Newman, Geoffrey Pascoe, Gordon Wyeth, Michael Milford, Alexander D. Stewart, Winston Churchill, Colin McManus, Alastair Harrison, Dan Barnes and Ingmar Posner and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The International Journal of Robotics Research and Oxford University Research Archive (ORA) (University of Oxford).

In The Last Decade

Will Maddern

19 papers receiving 1.7k citations

Hit Papers

align trajectories

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 year, 1000 km: The Oxford RobotCar dataset

2016 962 citations Will Maddern, Geoffrey Pascoe et al. The International Journal of Robotics Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Will Maddern United Kingdom	17	1.3k	1.1k	274	254	227	19	1.7k
Geoffrey Pascoe United Kingdom	9	861 0.7×	739 0.7×	183 0.7×	193 0.8×	165 0.7×	9	1.2k
Frank Moosmann Germany	11	961 0.7×	717 0.6×	169 0.6×	234 0.9×	366 1.6×	14	1.5k
Lionel Heng Switzerland	20	1.2k 0.9×	1.3k 1.1×	180 0.7×	239 0.9×	115 0.5×	25	1.7k
Luciano Spinello Germany	24	1.3k 1.0×	844 0.7×	336 1.2×	123 0.5×	165 0.7×	45	1.9k
Jiajun Deng China	18	1.4k 1.0×	628 0.6×	98 0.4×	201 0.8×	249 1.1×	62	1.9k
K. Madhava Krishna India	21	1.2k 0.9×	935 0.8×	132 0.5×	116 0.5×	89 0.4×	177	1.8k
Oleg Naroditsky United States	8	1.9k 1.4×	1.9k 1.6×	276 1.0×	277 1.1×	102 0.4×	11	2.3k
Xieyuanli Chen China	23	993 0.8×	1.1k 1.0×	203 0.7×	482 1.9×	463 2.0×	97	1.7k
Hans‐Joachim Wuensche Germany	20	908 0.7×	798 0.7×	176 0.6×	212 0.8×	477 2.1×	99	1.7k
Paulo Borges Australia	17	881 0.7×	516 0.5×	192 0.7×	152 0.6×	143 0.6×	65	1.2k

Countries citing papers authored by Will Maddern

Since Specialization

Citations

This map shows the geographic impact of Will Maddern'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 Will Maddern with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Will Maddern more than expected).

Fields of papers citing papers by Will Maddern

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Will Maddern. 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 Will Maddern. The network helps show where Will Maddern may publish in the future.

Co-authorship network of co-authors of Will Maddern

This figure shows the co-authorship network connecting the top 25 collaborators of Will Maddern. A scholar is included among the top collaborators of Will Maddern 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 Will Maddern. Will Maddern is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

Xu, Ning, et al.. (2024). Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models. 4568–4578. 4 indexed citations

Toft, Carl, Will Maddern, Akihiko Torii, et al.. (2020). Long-Term Visual Localization Revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(4). 2074–2088. 113 indexed citations

Maddern, Will, et al.. (2018). Adversarial training for adverse conditions: Robust metric localisation using appearance transfer. Oxford University Research Archive (ORA) (University of Oxford). 56 indexed citations

Maddern, Will, et al.. (2018). Mark Yourself: Road Marking Segmentation via Weakly-Supervised Annotations from Multimodal Data. 24 indexed citations

Barnes, Dan, Will Maddern, Geoffrey Pascoe, & Ingmar Posner. (2018). Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments. 1894–1900. 37 indexed citations

Pascoe, Geoffrey, Will Maddern, Michael Tanner, Pedro Piniés, & Paul Newman. (2017). NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance. 1446–1455. 39 indexed citations

Maddern, Will, et al.. (2016). 1 year, 1000 km: The Oxford RobotCar dataset. The International Journal of Robotics Research. 36(1). 3–15. 962 indexed citations breakdown →

Maddern, Will & Paul Newman. (2016). Real-time probabilistic fusion of sparse 3D LIDAR and dense stereo. 2181–2188. 66 indexed citations

Pascoe, Geoffrey, Will Maddern, Alexander D. Stewart, & Paul Newman. (2015). FARLAP: Fast robust localisation using appearance priors. 29 indexed citations

10.

Barnes, Dan, Will Maddern, & Ingmar Posner. (2015). Exploiting 3D semantic scene priors for online traffic light interpretation. 573–578. 27 indexed citations

11.

Maddern, Will, Geoffrey Pascoe, & Paul Newman. (2015). Leveraging experience for large-scale LIDAR localisation in changing cities. 1684–1691. 44 indexed citations

12.

Pascoe, Geoffrey, Will Maddern, & Paul Newman. (2015). Robust Direct Visual Localisation using Normalised Information Distance. 70.1–70.13. 26 indexed citations

13.

McManus, Colin, Winston Churchill, Will Maddern, Alexander D. Stewart, & Paul Newman. (2014). Shady dealings: Robust, long-term visual localisation using illumination invariance. 901–906. 89 indexed citations

14.

Upcroft, Ben, Colin McManus, Winston Churchill, Will Maddern, & Paul Newman. (2014). Lighting invariant urban street classification. 1712–1718. 20 indexed citations

15.

Maddern, Will, Alexander D. Stewart, & Paul Newman. (2014). LAPS-II: 6-DoF day and night visual localisation with prior 3D structure for autonomous road vehicles. 22 indexed citations

16.

Maddern, Will, Michael Milford, & Gordon Wyeth. (2012). Towards persistent indoor appearance-based localization, mapping and navigation using CAT-Graph. 29. 4224–4230. 11 indexed citations

17.

Maddern, Will, Michael Milford, & Gordon Wyeth. (2012). CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory. The International Journal of Robotics Research. 31(4). 429–451. 74 indexed citations

18.

Maddern, Will, Alastair Harrison, & Paul Newman. (2012). Lost in translation (and rotation): Rapid extrinsic calibration for 2D and 3D LIDARs. 3096–3102. 61 indexed citations

19.

Maddern, Will, Michael Milford, & Gordon Wyeth. (2011). Continuous appearance-based trajectory SLAM. 29. 3595–3600. 23 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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