HDR and PhD Theses

Note: HDR stands for Habilitation à Diriger des Recherches (HDR, French diploma required to supervise PhD students)

  1. 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
  2. Aravecchia, S. (2023). Map Quality Criteria for Autonomous Exploration in Natural Environment [PhD thesis]. Université de Lorraine. PDF
  3. Venkataramanan, A. (2023). Automatic identification of diatoms using deep learning to improve ecological diagnosis of aquatic environments [PhD thesis]. Université de Lorraine. PDF
  4. Ouabi, O.-L. (2022). Towards ultrasound-based localization and mapping for long-range inspection robots [PhD thesis]. Université de Lorraine. PDF
  5. Richard, A. (2022). On the Modeling of Dynamic-Systems using Sequence-based Deep Neural-Networks [PhD thesis]. Georgia Institue of Technology. PDF
  6. Chahine, G. (2021). Multi-sensor Mapping in natural environment: Three-Dimensional Reconstruction and temporal alignment [PhD thesis]. Georgia Institue of Technology. PDF
  7. Aouini, M. (2021). Predictive maintenance smart system based on ultrasonic guided waves and data mining [PhD thesis]. Université de Lorraine.
  8. Mahé, A. (2020). Neural network based system identification for model predictive control [PhD thesis]. CentraleSupélec. PDF
  9. Benbihi, A. (2020). Robust Visual Features for Long-Term Monitoring [PhD thesis]. CentraleSupélec. PDF
  10. Griffith, S. D. (2021). Map-centric visual data association across seasons in a natural environment [PhD thesis]. Georgia Institue of Technology. PDF
  11. Khazem, S. (2024). Deep learning and image processing for tree knot detection and prediction [PhD thesis]. CentraleSupélec. PDF

Journals

2024

  1. 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
  2. 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
  3. 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
  4. 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

  1. 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
  2. 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
  3. 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

  1. Chahine, G., & Pradalier, C. (2022). Semantic-aware spatio-temporal alignment of natural outdoor surveys. Field Robotics. PDF
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

  1. 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
  2. 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
  3. 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

  1. 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
  2. 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

  1. Cazau, D., Pradalier, C., Bonnel, J., & Guinet, C. (2017). Do southern elephant seals behave like weather buoys? Oceanography, 30(2), 140–149. PDF
  2. Griffith, S., Chahine, G., & Pradalier, C. (2017). Symphony lake dataset. The International Journal of Robotics Research, 36(11), 1151–1158. PDF
  3. 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
  4. Griffith, S., & Pradalier, C. (2017). Survey registration for long-term natural environment monitoring. Journal of Field Robotics, 34(1), 188–208. PDF

2016

  1. 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

  1. 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
  2. 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
  3. 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
  4. 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

  1. 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

  1. 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).
  2. 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
  3. 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
  4. Schroepfer, P., Gründling, J. P., Schauffel, N., Oehrl, S., Pape, S., Kuhlen, T. W., Weyers, B., Ellwart, T., & Pradalier, C. (2024). Navigating Real-World Complexity: A Multi-Medium System for Heterogeneous Robot Teams and Multi-Stakeholder Human-Robot Interaction. Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 630–638. PDF

2023

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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

  1. 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
  2. Richard, A., Aravecchia, S., Geist, M., & Pradalier, C. (2022). Learning behaviors through physics-driven latent imagination. Conference on Robot Learning, 1190–1199. PDF
  3. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

  1. 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.
  2. Chahine, G., & Pradalier, C. (2019). Laser-supported monocular visual tracking for natural environments. 2019 19th International Conference on Advanced Robotics (ICAR), 801–806. PDF
  3. 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
  4. 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.
  5. 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
  6. 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
  7. 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
  8. 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

  1. 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
  2. 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.
  3. 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
  4. 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
  5. 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

  1. Pradalier, C., & Griffith, S. (2017). Long-term monitoring of a natural environment using an automated data acquisition system. ILTER Network.
  2. Richard, A., Pradalier, C., van Couwenberghe, R., & Perez, V. V. (2017). Automated recognition of habitat classes from overhead imagery. ILTER Network.
  3. Pradalier, C., & Chahine, G. (2017). Long-term quantitative river shore monitoring using a portable imaging suite. ILTER Network.
  4. Pradalier, C., & Robin, V. (2017). Automated quantification of charcoal-particle content in peat samples for paleo-ecological studies. ILTER Network.
  5. 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.
  6. 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

  1. Griffith, S., & Pradalier, C. (2016). Reprojection Flow for Image Registration Across Seasons. BMVC. PDF
  2. 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

  1. 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
  2. Michalec, R., & Pradalier, C. (2014). Sidescan sonar aided inertial drift compensation in autonomous underwater vehicles. 2014 Oceans-St. John’s, 1–5. PDF
  3. Griffith, S., Drews, P., & Pradalier, C. (2014). Towards autonomous lakeshore monitoring. Experimental Robotics: The 14th International Symposium on Experimental Robotics, 545–557. PDF
  4. 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
  5. 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

  1. Pradalier, C., Richard, A., & Schroepfer, P. (2022). A Graph-based Approach to the Initial Guess of UWB Anchor Self-Calibration. PDF
  2. 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
  3. Richard, A., Mahé, A., Pradalier, C., Rozenstein, O., & Geist, M. (2019). A comprehensive benchmark of neural networks for system identification. PDF
  4. 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
  5. Griffith, S., & Pradalier, C. Towards Reprojection Flow for Image Registration Across Seasons. PDF
  6. Batista, L. F. W., Aravecchia, S., Hutchinson, S., & Pradalier, C. (2025). Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests. In Workshop on Field Robotics at the IEEE International Conference on Robotics and Automation (ICRA) 2025.

Technical reports

  1. Pradalier, C. (2017). A task scheduler for ROS [Research Report]. UMI 2958 GeorgiaTech-CNRS. https://hal.science/hal-01435823 PDF
  2. Deiss, O., & Pradalier, C. (2016). Hadoop for Roboticists [Research Report]. UMI 2958 GeorgiaTech-CNRS. https://hal.science/hal-01435882 PDF
  3. 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
  4. 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