Thierry Peynot
About
In The Last Decade
Thierry Peynot
47 papers receiving 515 citations
Peers
Comparison fields: 5 of 63
- Aerospace Engineering 298
- Computer Vision and Pattern Recognition 267
- Biomedical Engineering 96
- Artificial Intelligence 68
- Electrical and Electronic Engineering 67
Countries citing papers authored by Thierry Peynot
This map shows the geographic impact of Thierry Peynot'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 Thierry Peynot with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thierry Peynot more than expected).
Fields of papers citing papers by Thierry Peynot
This network shows the impact of papers produced by Thierry Peynot. 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 Thierry Peynot. The network helps show where Thierry Peynot may publish in the future.
Co-authorship network of co-authors of Thierry Peynot
This figure shows the co-authorship network connecting the top 25 collaborators of Thierry Peynot. A scholar is included among the top collaborators of Thierry Peynot 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 Thierry Peynot. Thierry Peynot 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 | 50 | |
| 3 | 2 | |
| 4 | 23 | |
| 5 | 9 | |
| 6 | 10 | |
| 7 | I2-S2: Intra-image-SeqSLAM for more accurate vision-based localisation in underground mines | 7 |
| 8 | Lidar-based detection of airborne particles for robust robot perception | 3 |
| 9 | 14 | |
| 10 | Enhancing underground visual place recognition with Shannon Entropy saliency | 12 |
| 11 | Towards robotic arthroscopy: ‘Instrument gap’ segmentation | 1 |
| 12 | Learned ultra-wideband RADAR sensor model for augmented LIDAR-based traversability mapping in vegetated environments | 5 |
| 13 | Characterisation of the Delphi Electronically Scanning Radar for robotics applications | 23 |
| 14 | Non-parametric consistency test for multiple-sensing-modality data fusion | 2 |
| 15 | Analyzing the impact of learning inputs on near-to-far terrain traversability estimation | 1 |
| 16 | Laser-to-radar sensing redundancy for resilient perception in adverse environmental conditions | 10 |
| 17 | Mawson the astrobiologist rover: towards automatic recognition of stromatolites | 3 |
| 18 | Datasets for the evaluation of multi-sensor perception in natural environments with challenging conditions | 1 |
| 19 | Towards Discrimination of Challenging Conditions for UGVs with Visual and Infrared Sensors | 2 |
| 20 | Sensor Data Integrity : Multi-Sensor Perception for Unmanned Ground Vehicles | 4 |
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.