| Characterisation of the Delphi Electronically Scanning Radar for robotics applications L Stanislas, T Peynot Proceedings of the Australasian conference on robotics and automation 2015, 1-10, 2015 | 51 | 2015 |
| Multimodal sensor fusion for robust obstacle detection and classification in the maritime RobotX challenge L Stanislas, M Dunbabin IEEE Journal of Oceanic Engineering 44 (2), 343-351, 2018 | 30 | 2018 |
| Airborne particle classification in lidar point clouds using deep learning L Stanislas, J Nubert, D Dugas, J Nitsch, N Sünderhauf, R Siegwart, ... Field and Service Robotics: Results of the 12th International Conference …, 2021 | 23 | 2021 |
| Lidar-based detection of airborne particles for robust robot perception L Stanislas, N Suenderhauf, T Peynot Proceedings of the Australasian Conference on Robotics and Automation (ACRA …, 2018 | 10 | 2018 |
| Bruce: An ASV solution for the 2016 maritime RobotX challenge L Stanislas, P Smith, B Tidd, P Kujala, S Nicholson, A Dawson, R Lamont, ... Proc. RobotX Forum, 2016 | 4 | 2016 |
| Detecting airborne particles in sensor data with deep learning for robust robot perception in adverse environments L Stanislas Queensland University of Technology, 2020 | 1 | 2020 |
| Bruce: A system-of-systems solution to the 2018 Maritime RobotX Challenge L Stanislas, K Moyle, E Corser, T Ha, R Dyson, R Lamont, M Dunbabin | 1 | 2018 |
| Processing LiDAR Data AR Knoll, HL Knoll, C Lee, T Whitney, LN Stanislas US Patent App. 18/512,881, 2024 | | 2024 |
Thierry PeynotQueensland University of Technology (QUT)Verified email at qut.edu.au
