Lei Tai
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Aerospace Engineering top 2%
- Control and Systems Engineering top 5%
- Automotive Engineering top 5%
- Co-authors
- Ming LiuGiuseppe PaoloShaohua LiMingyang LiKai SunYongjian ChenWolfram BurgardJingwei Zhang
- Topics
- Robotics and Sensor-Based Localization (7 papers)Advanced Neural Network Applications (5 papers)Reinforcement Learning in Robotics (5 papers)
In The Last Decade
Lei Tai
20 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Computer Vision and Pattern Recognition 1.0k
- Artificial Intelligence 582
- Aerospace Engineering 488
- Control and Systems Engineering 239
- Automotive Engineering 213
Countries citing papers authored by Lei Tai
This map shows the geographic impact of Lei Tai'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 Lei Tai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lei Tai more than expected).
Fields of papers citing papers by Lei Tai
This network shows the impact of papers produced by Lei Tai. 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 Lei Tai. The network helps show where Lei Tai may publish in the future.
Co-authorship network of co-authors of Lei Tai
This figure shows the co-authorship network connecting the top 25 collaborators of Lei Tai. A scholar is included among the top collaborators of Lei Tai 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 Lei Tai. Lei Tai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 2 | |
| 3 | 196 | |
| 4 | 10 | |
| 5 | 49 | |
| 6 | 57 | |
| 7 | 16 | |
| 8 | 16 | |
| 9 | 117 | |
| 10 | Neural SLAM | 7 |
| 11 | Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigationbreakdown → | 517 |
| 12 | 5 | |
| 13 | 11 | |
| 14 | 26 | |
| 15 | Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not. | 31 |
| 16 | 90 | |
| 17 | 90 | |
| 18 | 152 | |
| 19 | 1 | |
| 20 | Predicting financial distress of listed corporations based on fuzzy support vector machine | 3 |
About Lei Tai
Lei Tai is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence, having authored 20 papers that have together received 1.4k indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (7 papers), Advanced Neural Network Applications (5 papers) and Reinforcement Learning in Robotics (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.0k citations), Aerospace Engineering (488 citations) and Artificial Intelligence (582 citations). Lei Tai has collaborated with scholars based in Hong Kong, China and Germany. Frequent co-authors include Ming Liu, Giuseppe Paolo, Shaohua Li, Mingyang Li, Kai Sun, Yongjian Chen, Wolfram Burgard, Jingwei Zhang, Yun Peng and Yuan Wang. Their work appears in journals such as The International Journal of Advanced Manufacturing Technology, Journal of Materials Chemistry B 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.