HDR and PhD Theses
Note: HDR stands for Habilitation à Diriger des Recherches (HDR, French diploma required to supervise PhD students)
- Pradalier, C. (2015). Autonomous Mobile Systems for Long-Term Operations in Spatio-Temporal Environments [HDR de l’Institut National Polytechnique de Toulouse]. INP DE TOULOUSE. PDF
- Aravecchia, S. (2023). Map Quality Criteria for Autonomous Exploration in Natural Environment [PhD thesis]. Université de Lorraine. PDF
- Venkataramanan, A. (2023). Automatic identification of diatoms using deep learning to improve ecological diagnosis of aquatic environments [PhD thesis]. Université de Lorraine. PDF
- Ouabi, O.-L. (2022). Towards ultrasound-based localization and mapping for long-range inspection robots [PhD thesis]. Université de Lorraine. PDF
- Richard, A. (2022). On the Modeling of Dynamic-Systems using Sequence-based Deep Neural-Networks [PhD thesis]. Georgia Institue of Technology. PDF
- Chahine, G. (2021). Multi-sensor Mapping in natural environment: Three-Dimensional Reconstruction and temporal alignment [PhD thesis]. Georgia Institue of Technology. PDF
- Aouini, M. (2021). Predictive maintenance smart system based on ultrasonic guided waves and data mining [PhD thesis]. Université de Lorraine.
- Mahé, A. (2020). Neural network based system identification for model predictive control [PhD thesis]. CentraleSupélec. PDF
- Benbihi, A. (2020). Robust Visual Features for Long-Term Monitoring [PhD thesis]. CentraleSupélec. PDF
- Griffith, S. D. (2021). Map-centric visual data association across seasons in a natural environment [PhD thesis]. Georgia Institue of Technology. PDF
- Khazem, S. (2024). Deep learning and image processing for tree knot detection and prediction [PhD thesis]. CentraleSupélec. PDF
Journals
2024
- Aravecchia, S., Clausel, M., & Pradalier, C. (2024). Comparing metrics for evaluating 3D map quality in natural environments. Robotics and Autonomous Systems, 173, 104617. https://doi.org/10.1016/j.robot.2023.104617 https://hal.science/hal-04128242
- Courcoul, C., Boulêtreau, S., Bec, A., Danger, M., Felten, V., Pradalier, C., Roche-Bril, M., & Leflaive, J. (2024). Flow intermittency affects the nutritional quality of phototrophic biofilms and their capacity to support secondary production. Freshwater Biology, 69(1), 84–99. PDF
- Klopffer, L., Louvet, N., Becker, S., Fix, J., Pradalier, C., & Mathieu, L. (2024). Effect of shear rate on early Shewanella oneidensis bacterial adhesion dynamics monitored by Deep Learning. Biofilm, 100240. https://doi.org/10.1016/j.bioflm.2024.100240
- Venkataramanan, A., Kloster, M., Burfeid-Castellanos, A., Dani, M., Mayombo, N. A. S., Vidakovic, D., Langenkämper, D., Tan, M., Pradalier, C., Nattkemper, T., Laviale, M., & Beszteri, B. (2024). “UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models. GigaScience, 13. https://doi.org/10.1093/gigascience/giae087
2023
- Khazem, S., Richard, A., Fix, J., & Pradalier, C. (2023). Deep learning for the detection of semantic features in tree X-ray CT scans. Artificial Intelligence in Agriculture, 7, 13–26. PDF
- Venkataramanan, A., Faure-Giovagnoli, P., Regan, C., Heudre, D., Figus, C., Usseglio-Polatera, P., Pradalier, C., & Laviale, M. (2023). Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach. Engineering Applications of Artificial Intelligence, 117, 105594. PDF
- Le Gentil, C., Ouabi, O.-L., Wu, L., Pradalier, C., & Vidal-Calleja, T. (2023). Accurate Gaussian-Process-based Distance Fields with Applications to Echolocation and Mapping. IEEE Robotics and Automation Letters. PDF
2022
- Chahine, G., & Pradalier, C. (2022). Semantic-aware spatio-temporal alignment of natural outdoor surveys. Field Robotics. PDF
- Venkataramanan, A., Richard, A., & Pradalier, C. (2022). A data driven approach to generate realistic 3D tree barks. Graphical Models, 123, 101166. https://doi.org/10.1016/j.gmod.2022.101166 PDF
- Orenstein, E. C., Ayata, S.-D., Maps, F., Becker, É. C., Benedetti, F., Biard, T., de Garidel-Thoron, T., Ellen, J. S., Ferrario, F., Giering, S. L. C., & others. (2022). Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, 67(8), 1647–1669. https://doi.org/10.1002/lno.12101
- Chahine, G., Schroepfer, P., Ouabi, O.-L., & Pradalier, C. (2022). A magnetic crawler system for autonomous long-range inspection and maintenance on large structures. Sensors, 22(9), 3235. https://doi.org/10.3390/s22093235 PDF
- Ouabi, O.-L., Pomarede, P., Declercq, N. F., Zeghidour, N., Geist, M., & Pradalier, C. (2022). Learning the propagation properties of rectangular metal plates for Lamb wave-based mapping. Ultrasonics, 123, 106705. PDF
- Guilloteau, H., Pradalier, C., Roman, V. L., Bellanger, X., Billard, P., & Merlin, C. (2022). Identification of antibiotics triggering the dissemination of antibiotic resistance genes by SXT/R391 elements using a dedicated high-throughput whole-cell biosensor assay. Journal of Antimicrobial Chemotherapy, 77(1), 112–123. PDF
- Fine, L., Richard, A., Tanny, J., Pradalier, C., Rosa, R., & Rozenstein, O. (2022). Introducing state-of-the-art deep learning technique for gap-filling of eddy covariance crop evapotranspiration data. Water, 14(5), 763. https://doi.org/10.3390/w14050763
2021
- Oliveira, C., Aravecchia, S., Pradalier, C., Robin, V., & Devin, S. (2021). The use of remote sensing tools for accurate charcoal kilns’ inventory and distribution analysis: Comparative assessment and prospective. International Journal of Applied Earth Observation and Geoinformation, 105, 102641. PDF
- Richard, A., Aravecchia, S., Schillaci, T., Geist, M., & Pradalier, C. (2021). How to train your heron. IEEE Robotics and Automation Letters, 6(3), 5247–5252. https://arxiv.org/abs/2102.10357
- Ouabi, O.-L., Pomarede, P., Geist, M., Declercq, N. F., & Pradalier, C. (2021). A fastslam approach integrating beamforming maps for ultrasound-based robotic inspection of metal structures. IEEE Robotics and Automation Letters, 6(2), 2908–2913. PDF
- Chahine, G., Vaidis, M., Pomerleau, F., & Pradalier, C. (2021). Mapping in unstructured natural environment: A sensor fusion framework for wearable sensor suites. SN Applied Sciences, 3, 1–14. PDF
- Mahé, A., Richard, A., Aravecchia, S., Geist, M., & Pradalier, C. (2021). Evaluation of prioritized deep system identification on a path following task. Journal of Intelligent & Robotic Systems, 101(4), 78. PDF
2020
- Pretto, A., Aravecchia, S., Burgard, W., Chebrolu, N., Dornhege, C., Falck, T., Fleckenstein, F., Fontenla, A., Imperoli, M., Khanna, R., & others. (2020). Building an aerial–ground robotics system for precision farming: an adaptable solution. IEEE Robotics & Automation Magazine, 28(3), 29–49. http://arxiv.org/abs/1911.03098
- Wu, X., Aravecchia, S., Lottes, P., Stachniss, C., & Pradalier, C. (2020). Robotic weed control using automated weed and crop classification. Journal of Field Robotics, 37(2), 322–340. PDF
- Griffith, S., Dellaert, F., & Pradalier, C. (2020). Transforming multiple visual surveys of a natural environment into time-lapses. The International Journal of Robotics Research, 39(1), 100–126. https://hal.archives-ouvertes.fr/hal-02278909
2018
- Pradalier, C., Juan, P.-A., McCabe, R. J., & Capolungo, L. (2018). A graph theory-based automated twin recognition technique for electron backscatter diffraction analysis. Integrating Materials and Manufacturing Innovation, 7, 12–27. PDF
- Benbihi, A., Geist, M., & Pradalier, C. (2018). Deep Representation Learning for Domain Adaptation of Semantic Image Segmentation. ArXiv Preprint ArXiv:1805.04141. https://arxiv.org/abs/1805.04141
2017
- Cazau, D., Pradalier, C., Bonnel, J., & Guinet, C. (2017). Do southern elephant seals behave like weather buoys? Oceanography, 30(2), 140–149. PDF
- Griffith, S., Chahine, G., & Pradalier, C. (2017). Symphony lake dataset. The International Journal of Robotics Research, 36(11), 1151–1158. PDF
- El Gmili, Y., Bonanno, P. L., Sundaram, S., Li, X., Puybaret, R., Patriarche, G., Pradalier, C., Decobert, J., Voss, P. L., Salvestrini, J.-P., & others. (2017). Mask effect in nano-selective-area-growth by MOCVD on thickness enhancement, indium incorporation, and emission of InGaN nanostructures on AlN-buffered Si (111) substrates. Optical Materials Express, 7(2), 376–385. PDF
- Griffith, S., & Pradalier, C. (2017). Survey registration for long-term natural environment monitoring. Journal of Field Robotics, 34(1), 188–208. PDF
2016
- Sundaram, S., Li, X., El Gmili, Y., Bonanno, P. L., Puybaret, R., Pradalier, C., Pantzas, K., Patriarche, G., Voss, P. L., Salvestrini, J.-P., & others. (2016). Single-crystal nanopyramidal BGaN by nanoselective area growth on AlN/Si (111) and GaN templates. Nanotechnology, 27(11), 115602. PDF
2015 and earlier
- Bertin, N., Upadhyay, M. V., Pradalier, C., & Capolungo, L. (2015). A FFT-based formulation for efficient mechanical fields computation in isotropic and anisotropic periodic discrete dislocation dynamics. Modelling and Simulation in Materials Science and Engineering, 23(6), 065009. PDF
- Juan, P.-A., Pradalier, C., Berbenni, S., McCabe, R. J., Tomé, C. N., & Capolungo, L. (2015). A statistical analysis of the influence of microstructure and twin–twin junctions on twin nucleation and twin growth in Zr. Acta Materialia, 95, 399–410. PDF
- Garneau, M.-È., Posch, T., Hitz, G., Pomerleau, F., Pradalier, C., Siegwart, R., & Pernthaler, J. (2013). Short-term displacement of Planktothrix rubescens (cyanobacteria) in a pre-alpine lake observed using an autonomous sampling platform. Limnology and Oceanography, 58(5), 1892–1906. PDF
- Jacobson, A., Panozzo, D., Glauser, O., Pradalier, C., Hilliges, O., & Sorkine-Hornung, O. (2014). Tangible and modular input device for character articulation. ACM Transactions on Graphics (TOG), 33(4), 1–12. PDF
Conferences
2025
- Sedeh, M. A., Benbihi, A., Martin, R., Clausel, M., & Pradalier, C. (2025). AttriVision: Advancing Generalization in Pedestrian Attribute Recognition using CLIP. Proceedings of the Winter Conference on Applications of Computer Vision, 354–365.
2024
- Barros, T., Premebida, C., Aravecchia, S., Pradalier, C., & Nunes, U. J. (2024). SPVSoAP3D: A Second-order Average Pooling Approach to enhance 3D Place Recognition in Horticultural Environments. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
- Batista, L. F. W., Ro, J., Richard, A., Schroepfer, P., Hutchinson, S., & Pradalier, C. (2024). A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1258–1264. https://doi.org/10.1109/IROS58592.2024.10802067 PDF
- Batista, L. F. W., Khazem, S., Adibi, M., Hutchinson, S., & Pradalier, C. (2024). PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects. Proceedings of the IEEE/CVF European Conference on Computer Vision TRICKY Workshop. https://arxiv.org/abs/2409.12659
2023
- Aravecchia, S., Richard, A., Clausel, M., & Pradalier, C. (2023). Next-Best-View selection from observation viewpoint statistics. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 10505–10510. PDF
- Khazem, S., Fix, J., & Pradalier, C. (2023). Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation. International Conference on Computer Vision Systems, 169–180. https://arxiv.org/pdf/2308.11291.pdf
- Aravecchia, S., Richard, A., Clausel, M., & Pradalier, C. (2023). Next-Best-View selection from observation viewpoint statistics. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 10505–10510. PDF
- Aravecchia, S., Richard, A., Clausel, M., & Pradalier, C. (2023). Measuring 3D-reconstruction quality in probabilistic volumetric maps with the Wasserstein Distance. ISR Europe 2023; 56th International Symposium on Robotics, 161–167. PDF
- Schroepfer, P., Chahine, G., & Pradalier, C. (2023). 6DoF State Estimation with a Mesh Constrained Particle Filter For Wheeled Robots. ISR Europe 2023; 56th International Symposium on Robotics, 155–160. PDF
- Venkataramanan, A., Laviale, M., & Pradalier, C. (2023). Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval. International Conference on Computer Vision Systems, 422–431. https://arxiv.org/pdf/2308.08431.pdf
- Venkataramanan, A., Benbihi, A., Laviale, M., & Pradalier, C. (2023). Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers. Proceedings of the IEEE/CVF International Conference on Computer Vision, 4488–4497. PDF
- Schroepfer, P., & Pradalier, C. (2023). Why There is No Definition of Trust: A Systems Approach With a Metamodel Representation. 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1245–1251. PDF
- Schroepfer, P., Schauffel, N., Gründling, J., Ellwart, T., Weyers, B., & Pradalier, C. (2023). Trust and Acceptance of Multi-Robot Systems "in the Wild". A Roadmap exemplified within the EU-Project BugWright2. ArXiv Preprint ArXiv:2312.08047. https://arxiv.org/abs/2312.08047
2022
- Ouabi, O.-L., Ridani, A., Pomarede, P., Zeghidour, N., Declercq, N. F., Geist, M., & Pradalier, C. (2022). Combined Grid and Feature-based Mapping of Metal Structures with Ultrasonic Guided Waves. 2022 International Conference on Robotics and Automation (ICRA), 5056–5062. PDF
- Richard, A., Aravecchia, S., Geist, M., & Pradalier, C. (2022). Learning behaviors through physics-driven latent imagination. Conference on Robot Learning, 1190–1199. PDF
- Richard, A., Rozenstein, O., Fine, L., Malachy, N., Pradalier, C., & Tanny, J. (2022). Data-Driven Estimation of Actual Evapotranspiration to Support Irrigation Management. AI for Agriculture and Food Systems. https://doi.org/10.1016/j.agwat.2023.108317
2021
- Chahine, G., & Wishon, M. J. (2021). Detecting overlapping semiconductor nanopillars and characterization. 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET), 55–59. PDF
- Ouabi, O.-L., Pribić, R., & Olaru, S. (2021). Stochastic complex-valued neural networks for radar. 2020 28th European Signal Processing Conference (EUSIPCO), 1442–1446. PDF
- Ouabi, O.-L., Pomarede, P., Geist, M., Declercq, N. F., & Pradalier, C. (2021). Monte-carlo localization on metal plates based on ultrasonic guided waves. Experimental Robotics: The 17th International Symposium, 345–353. PDF
- Ridani, A., Ouabi, O.-L., Declercq, N. F., & Pradalier, C. (2021). On-plate autonomous exploration for an inspection robot using ultrasonic guided waves. 2021 European Conference on Mobile Robots (ECMR), 1–6. PDF
- Venkataramanan, A., Laviale, M., Figus, C., Usseglio-Polatera, P., & Pradalier, C. (2021). Tackling inter-class similarity and intra-class variance for microscopic image-based classification. International Conference on Computer Vision Systems, 93–103. https://arxiv.org/abs/2109.11891
- Richard, A., Fine, L., Rozenstein, O., Tanny, J., Geist, M., & Pradalier, C. (2021). Filling gaps in micro-meteorological data. Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V, 101–117. PDF
- Pradalier, C., Aravecchia, S., & Pomerleau, F. (2021). Multi-session lake-shore monitoring in visually challenging conditions. Field and Service Robotics: Results of the 12th International Conference, 1–14. PDF
- Fleckenstein, F., Winterhalter, W., Dornhege, C., Pradalier, C., & Burgard, W. (2021). Smooth local planning incorporating steering constraints. Field and Service Robotics: Results of the 12th International Conference, 443–457. PDF
2020
- Oliveira, C., Aravecchia, S., May, L., Pradalier, C., Robin, V., & Devin, S. (2020). Towards an automatic detection of charcoal production platforms in airborne LiDAR images. 5ème Colloque Des Zones Ateliers-CNRS. PDF
- Pradalier, C., Ouabi, O.-L., Pomarede, P., & Steckel, J. (2020). On-plate localization and mapping for an inspection robot using ultrasonic guided waves: A proof of concept. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5045–5050. PDF
- Wu, X., Vela, P. A., & Pradalier, C. (2020). Robust monocular edge visual odometry through coarse-to-fine data association. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4923–4929. PDF
- Benbihi, A., Arravechia, S., Geist, M., & Pradalier, C. (2020). Image-based place recognition on bucolic environment across seasons from semantic edge description. 2020 IEEE International Conference on Robotics and Automation (ICRA), 3032–3038. https://arxiv.org/abs/1910.12468
- Pradalier, C., Ouabi, O.-L., Pomarede, P., & Steckel, J. (2020). On-plate localization and mapping for an inspection robot using ultrasonic guided waves: A proof of concept. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5045–5050. PDF
2019
- Bellanger, X., Pradalier, C., Guilloteau, H., Roman, V., & Merlin, C. (2019). Identification d’antibiotiques stimulant la dissémination de gènes d’antibiorésistance en concentrations sub-inhibitrices et conséquences au niveau environnemental. IXe Colloque De l’Association Francophone d’Ecologie Microbienne.
- Chahine, G., & Pradalier, C. (2019). Laser-supported monocular visual tracking for natural environments. 2019 19th International Conference on Advanced Robotics (ICAR), 801–806. PDF
- Mahé, A., Richard, A., Mouscadet, B., Pradalier, C., & Geist, M. (2019). Importance sampling for deep system identification. 2019 19Th International Conference on Advanced Robotics (ICAR), 43–48. PDF
- Bellanger, X., Pradalier, C., Guilloteau, H., Roman, V., & Merlin, C. (2019). When antibiotics stimulate the spread of resistance genes: small doses and dramatic effects. 15e Congrès National De La Societe Française De Microbiologie.
- Benbihi, A., Geist, M., & Pradalier, C. (2019). Elf: Embedded localisation of features in pre-trained cnn. Proceedings of the IEEE/CVF International Conference on Computer Vision, 7940–7949. https://arxiv.org/pdf/1907.03261.pdf
- Benbihi, A., Geist, M., & Pradalier, C. (2019). Learning sensor placement from demonstration for UAV networks. 2019 IEEE Symposium on Computers and Communications (ISCC), 1–6. https://arxiv.org/pdf/1909.01636.pdf
- Wu, X., & Pradalier, C. (2019). Illumination robust monocular direct visual odometry for outdoor environment mapping. 2019 International Conference on Robotics and Automation (ICRA), 2392–2398. PDF
- Wu, X., Aravecchia, S., & Pradalier, C. (2019). Design and implementation of computer vision based in-row weeding system. 2019 International Conference on Robotics and Automation (ICRA), 4218–4224. PDF
2018
- Wu, X., & Pradalier, C. (2018). Multi-scale direct sparse visual odometry for large-scale natural environment. 2018 International Conference on 3D Vision (3DV), 89–97. PDF
- Bellanger, X., Pradalier, C., Guilloteau, H., Barrón, M. de L. C., Roman, V., & Merlin, C. (2018). Identifying antibiotics triggering the dissemination of resistance genes at low concentrations. Xenowac II.
- Mahé, A., Pradalier, C., & Geist, M. (2018). Trajectory-control using deep system identification and model predictive control for drone control under uncertain load. 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), 753–758. PDF
- Richard, A., Benbihi, A., Pradalier, C., Perez, V., & Van Couwenberghe, R. (2018). Automated segmentation of land use from overhead imagery. International Conference on Precision Agriculture. PDF
- Chahine, G., & Pradalier, C. (2018). Survey of monocular SLAM algorithms in natural environments. 2018 15th Conference on Computer and Robot Vision (CRV), 345–352. PDF
2017
- Pradalier, C., & Griffith, S. (2017). Long-term monitoring of a natural environment using an automated data acquisition system. ILTER Network.
- Richard, A., Pradalier, C., van Couwenberghe, R., & Perez, V. V. (2017). Automated recognition of habitat classes from overhead imagery. ILTER Network.
- Pradalier, C., & Chahine, G. (2017). Long-term quantitative river shore monitoring using a portable imaging suite. ILTER Network.
- Pradalier, C., & Robin, V. (2017). Automated quantification of charcoal-particle content in peat samples for paleo-ecological studies. ILTER Network.
- Scornec, H., Guilloteau, H., Groshenry, G., Pradalier, C., Bellanger, X., & Merlin, C. (2017). Exploring the effect of antibiotics at sub-MIC level on the activity of mobile genetic elements. 4th EDAR.
- Scornec, H., Guilloteau, H., Groshenry, G., Pradalier, C., Bellanger, X., & Merlin, C. (2017). When sub-inhibitory concentrations of antibiotics promote the dissemination of unselected resistance genes. FEMS 2017.
2016
- Griffith, S., & Pradalier, C. (2016). Reprojection Flow for Image Registration Across Seasons. BMVC. PDF
- Griffith, S., & Pradalier, C. (2016). A spatially and temporally scalable approach for long-term lakeshore monitoring. Field and Service Robotics: Results of the 10th International Conference, 3–16. PDF
2015 and earlier
- Griffith, S., Dellaert, F., & Pradalier, C. (2015). Robot-enabled lakeshore monitoring using visual SLAM and SIFT flow. RSS Workshop on Multi-View Geometry in Robotics. PDF
- Michalec, R., & Pradalier, C. (2014). Sidescan sonar aided inertial drift compensation in autonomous underwater vehicles. 2014 Oceans-St. John’s, 1–5. PDF
- Griffith, S., Drews, P., & Pradalier, C. (2014). Towards autonomous lakeshore monitoring. Experimental Robotics: The 14th International Symposium on Experimental Robotics, 545–557. PDF
- Sommer, H., Pradalier, C., & Furgale, P. (2013). Automatic differentiation on differentiable manifolds as a tool for robotics. Robotics Research: The 16th International Symposium ISRR, 505–520. PDF
- Siegenthaler, C., Pradalier, C., Günther, F., Hitz, G., & Siegwart, R. (2013). System integration and fin trajectory design for a robotic sea-turtle. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3790–3795. PDF
Other publications
- Pradalier, C., Richard, A., & Schroepfer, P. (2022). A Graph-based Approach to the Initial Guess of UWB Anchor Self-Calibration. PDF
- Wu, X., & Pradalier, C. (2019). Robust semi-direct monocular visual odometry using edge and illumination-robust cost. In arXiv preprint arXiv:1909.11362 (Vol. 18). https://arxiv.org/pdf/1909.11362.pdf
- Richard, A., Mahé, A., Pradalier, C., Rozenstein, O., & Geist, M. (2019). A comprehensive benchmark of neural networks for system identification. PDF
- Wu, X., Benbihi, A., Richard, A., & Pradalier, C. (2019). Semantic nearest neighbor fields monocular edge visual-odometry. In arXiv preprint arXiv:1904.00738. https://arxiv.org/abs/1904.00738
- Griffith, S., & Pradalier, C. Towards Reprojection Flow for Image Registration Across Seasons. PDF
Technical reports
- Pradalier, C. (2017). A task scheduler for ROS [Research Report]. UMI 2958 GeorgiaTech-CNRS. https://hal.science/hal-01435823 PDF
- Deiss, O., & Pradalier, C. (2016). Hadoop for Roboticists [Research Report]. UMI 2958 GeorgiaTech-CNRS. https://hal.science/hal-01435882 PDF
- Chatel, S., & Pradalier, C. (2016). CS8903 Special Problem : Mesh Networks for robotic teleoperation -State of the Art and Implementation for Robotics [Research Report]. UMI 2958 GeorgiaTech-CNRS. https://hal.science/hal-01435881 PDF
- Gout, A., Lifchitz, Y., Cottencin, T., Groshens, Q., Fix, J., & Pradalier, C. (2017). Evaluation of Off-The-Shelf CNNs for the Representation of Natural Scenes with Large Seasonal Variations [Research Report]. UMI 2958 GeorgiaTech-CNRS ; CentraleSupélec UMI GT-CNRS 2958 Université Paris-Saclay. https://hal.science/hal-01448091 PDF