Mathias Bürki
- Aerospace Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Geology top 10%
- Environmental Engineering
- Co-authors
- Roland SiegwartPaul FurgaleCésar CadenaIgor GilitschenskiJuan NietoMichael BosseRoland PhilippsenRenaud Dubé
- Topics
- Robotics and Sensor-Based Localization (11 papers)Advanced Image and Video Retrieval Techniques (6 papers)Indoor and Outdoor Localization Technologies (5 papers)
- Journals
- Journal of Field RoboticsCase Reports in NeurologyRepository for Publications and Research Data (ETH Zurich)
- Partner nations
- SwitzerlandSwedenGermany
In The Last Decade
Mathias Bürki
12 papers receiving 255 citations
Peers
Comparison fields: 5 of 27
- Aerospace Engineering 235
- Computer Vision and Pattern Recognition 209
- Electrical and Electronic Engineering 75
- Geology 33
- Environmental Engineering 31
Countries citing papers authored by Mathias Bürki
This map shows the geographic impact of Mathias Bürki'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 Mathias Bürki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathias Bürki more than expected).
Fields of papers citing papers by Mathias Bürki
This network shows the impact of papers produced by Mathias Bürki. 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 Mathias Bürki. The network helps show where Mathias Bürki may publish in the future.
Co-authorship network of co-authors of Mathias Bürki
This figure shows the co-authorship network connecting the top 25 collaborators of Mathias Bürki. A scholar is included among the top collaborators of Mathias Bürki 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 Mathias Bürki. Mathias Bürki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Fast and Accurate Mapping for Autonomous Racing. | 2 |
| 2 | 2 | |
| 3 | 13 | |
| 4 | 43 | |
| 5 | 10 | |
| 6 | 1 | |
| 7 | 24 | |
| 8 | 16 | |
| 9 | 20 | |
| 10 | 26 | |
| 11 | 73 | |
| 12 | 37 |
About Mathias Bürki
Mathias Bürki is a scholar working on Aerospace Engineering, Computer Vision and Pattern Recognition and Geology, having authored 12 papers that have together received 267 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (11 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Indoor and Outdoor Localization Technologies (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (209 citations), Aerospace Engineering (235 citations) and Geology (33 citations). Mathias Bürki has collaborated with scholars based in Switzerland, Sweden and Germany. Frequent co-authors include Roland Siegwart, Paul Furgale, César Cadena, Igor Gilitschenski, Juan Nieto, Michael Bosse, Roland Philippsen, Renaud Dubé, Elena Stumm and Lionel Heng. Their work appears in journals such as Journal of Field Robotics, Case Reports in Neurology and Repository for Publications and Research Data (ETH Zurich).
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.