David J. Yoon

462 total citations
13 papers, 304 citations indexed

About

David J. Yoon is a scholar working on Aerospace Engineering, Automotive Engineering and Artificial Intelligence. According to data from OpenAlex, David J. Yoon has authored 13 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Aerospace Engineering, 6 papers in Automotive Engineering and 6 papers in Artificial Intelligence. Recurrent topics in David J. Yoon's work include Robotics and Sensor-Based Localization (7 papers), Autonomous Vehicle Technology and Safety (6 papers) and Anomaly Detection Techniques and Applications (4 papers). David J. Yoon is often cited by papers focused on Robotics and Sensor-Based Localization (7 papers), Autonomous Vehicle Technology and Safety (6 papers) and Anomaly Detection Techniques and Applications (4 papers). David J. Yoon collaborates with scholars based in Canada, China and United States. David J. Yoon's co-authors include Timothy D. Barfoot, Angela P. Schoellig, Tim Barfoot, Shichen Lu, Andrew Lambert, Haowei Zhang, Soeren Kammel, Patrick McGarey, François Pomerleau and Haowei Zhang and has published in prestigious journals such as The International Journal of Robotics Research, IEEE Robotics and Automation Letters and Journal of Field Robotics.

In The Last Decade

David J. Yoon

12 papers receiving 296 citations

Peers

David J. Yoon

AE

CVPR

EEE

AI

EE

Paul Murcutt United Kingdom

Josip Ćesić Croatia

Yingying Ran China

Mingxing Wen Singapore

Sihan Chen China

Ziyang Hong China

Younghun Cho South Korea

Martin Holder Germany

Ian Baldwin United Kingdom

Igor Cvišić Croatia

Paul Murcutt United Kingdom

David J. Yoon

198 ×1.0

AE

140 ×1.4

CVPR

72 ×1.0

EEE

37 ×0.6

AI

52 ×1.0

EE

Citations per year, relative to David J. Yoon David J. Yoon (= 1×) peers Paul Murcutt

Countries citing papers authored by David J. Yoon

Since Specialization

Citations

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

Fields of papers citing papers by David J. Yoon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Yoon. 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 David J. Yoon. The network helps show where David J. Yoon may publish in the future.

Co-authorship network of co-authors of David J. Yoon

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

All Works

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13 of 13 papers shown

1.

Yoon, David J., et al.. (2025). Towards Fast Correspondence-Free Odometry Using Multiple FMCW Lidars. IEEE Robotics and Automation Letters. 10(9). 9088–9095.

2.

Yoon, David J., et al.. (2024). Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?. IEEE Robotics and Automation Letters. 10(1). 224–231. 5 indexed citations

3.

Yoon, David J., et al.. (2023). Need for Speed: Fast Correspondence-Free Lidar-Inertial Odometry Using Doppler Velocity. SPIRE - Sciences Po Institutional REpository. 5304–5310. 11 indexed citations

4.

Yoon, David J. & Timothy D. Barfoot. (2023). Towards Consistent Batch State Estimation Using a Time-Correlated Measurement Noise Model. 27. 3962–3968. 1 indexed citations

5.

Yoon, David J., Haowei Zhang, Shichen Lu, et al.. (2023). Boreas: A multi-season autonomous driving dataset. The International Journal of Robotics Research. 42(1-2). 33–42. 81 indexed citations

6.

Yoon, David J., et al.. (2022). Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?. IEEE Robotics and Automation Letters. 7(4). 10328–10335. 50 indexed citations

7.

Yoon, David J., et al.. (2022). Picking up Speed: Continuous-Time Lidar-Only Odometry Using Doppler Velocity Measurements. IEEE Robotics and Automation Letters. 8(1). 264–271. 20 indexed citations

8.

Yoon, David J., et al.. (2021). Unsupervised Learning of Lidar Features for Use ina Probabilistic Trajectory Estimator. IEEE Robotics and Automation Letters. 6(2). 2130–2138. 10 indexed citations

9.

Yoon, David J., et al.. (2021). Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning. 49 indexed citations

10.

Yoon, David J., et al.. (2020). A Data-Driven Motion Prior for Continuous-Time Trajectory Estimation on SE(3). IEEE Robotics and Automation Letters. 5(2). 1429–1436. 19 indexed citations

11.

Yoon, David J., et al.. (2020). Variational Inference With Parameter Learning Applied to Vehicle Trajectory Estimation. IEEE Robotics and Automation Letters. 5(4). 5291–5298. 18 indexed citations

12.

Yoon, David J., et al.. (2019). A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on <italic>SE(3)</italic>. IEEE Robotics and Automation Letters. 4(2). 594–601. 27 indexed citations

13.

McGarey, Patrick, et al.. (2018). Developing and deploying a tethered robot to map extremely steep terrain. Journal of Field Robotics. 35(8). 1327–1341. 13 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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