Yulun Tian
- Aerospace Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Mechanical Engineering
- Computer Networks and Communications top 10%
- Co-authors
- Jonathan P. HowCarlos Nieto-GrandaYun ChangLuca CarloneKasra KhosoussiDavid M. RosenKatherine LiuNicholas Roy
- Topics
- Robotics and Sensor-Based Localization (10 papers)Indoor and Outdoor Localization Technologies (5 papers)Advanced Vision and Imaging (2 papers)
- Journals
- The International Journal of Robotics ResearchIEEE Transactions on RoboticsIEEE Robotics and Automation Letters
- Partner nations
- United States
In The Last Decade
Yulun Tian
10 papers receiving 356 citations
Hit Papers
Peers
Comparison fields: 5 of 37
- Aerospace Engineering 275
- Computer Vision and Pattern Recognition 185
- Electrical and Electronic Engineering 85
- Mechanical Engineering 74
- Computer Networks and Communications 71
Countries citing papers authored by Yulun Tian
This map shows the geographic impact of Yulun Tian'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 Yulun Tian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yulun Tian more than expected).
Fields of papers citing papers by Yulun Tian
This network shows the impact of papers produced by Yulun Tian. 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 Yulun Tian. The network helps show where Yulun Tian may publish in the future.
Co-authorship network of co-authors of Yulun Tian
This figure shows the co-authorship network connecting the top 25 collaborators of Yulun Tian. A scholar is included among the top collaborators of Yulun Tian 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 Yulun Tian. Yulun Tian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 18 | |
| 3 | 7 | |
| 4 | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systemsbreakdown → | 150 |
| 5 | 8 | |
| 6 | 50 | |
| 7 | 75 | |
| 8 | 16 | |
| 9 | 27 | |
| 10 | 13 | |
| 11 | 2 |
About Yulun Tian
Yulun Tian is a scholar working on Aerospace Engineering, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 366 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (10 papers), Indoor and Outdoor Localization Technologies (5 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Aerospace Engineering (275 citations), Computer Vision and Pattern Recognition (185 citations) and Geology (29 citations). Yulun Tian has collaborated with scholars based in United States. Frequent co-authors include Jonathan P. How, Carlos Nieto-Granda, Yun Chang, Luca Carlone, Kasra Khosoussi, David M. Rosen, Katherine Liu, Nicholas Roy, Loc Tran and Kyel Ok. Their work appears in journals such as The International Journal of Robotics Research, IEEE Transactions on Robotics and IEEE Robotics and Automation Letters.
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.