Johann Laconte

Johann Laconte

Started on
laconte.johann@gmail.com

Expertise: Lidar Modeling, Traversability, Applied Mathematics, ICP, Iterative Closest Point

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Johann Laconte is currently a Ph.D student in robotics at Institut Pascal, France. He got an Engineering degree in computer sciences and modelisation from ISIMA (Institut Supérieur d’Informatique, de Modélisation et de leurs Applications) as well as a Master’s degree in Robotics from Université d’Auvergne, France, in 2018. He did an internship at Thales, during which he participated in the development of LIDAR SLAM algorithm. He also did an research internship at Norlab, working on the characterization of lidar measurements’ biases. His research interests include risk assessment, traversability, applied mathematics, autonomous vehicles, lidar modeling and path planning.

Education

  • M.Sc. in Robotics and Artificial Perception - University of Auvergne (UCA), 2018
  • Engineering degree in Computer Sciences and Modelisation - Institut Supérieur d’Informatique, de Modélisation et de leurs Applications (ISIMA), 2018

Publications

Journal Articles

  1. Baril, D., Deschênes, S.-P., Gamache, O., Vaidis, M., LaRocque, D., Laconte, J., Kubelka, V., Giguère, P., & Pomerleau, F. (2022). Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned. Field Robotics, 2(1), 1628–1660. https://doi.org/10.55417/fr.2022050 Accepted June 7 2022, ID FR-21-0050.R2, arXiv preprint arXiv:2111.13981
     PDF Publisher  Bibtex source
  2. Laconte, J., Kasmi, A., Pomerleau, F., Chapuis, R., Malaterre, L., Debain, C., & Aufrère, R. (2021). A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments. Sensors, 21(22), 7562.
     Bibtex source
  3. Labussière, M., Laconte, J., & Pomerleau, F. (2020). Geometry Preserving Sampling Method based on Spectral Decomposition for Large-scale Environments. Frontiers in Robotics and AI – Multi-Robot Systems, 7, 134. https://doi.org/10.3389/frobt.2020.572054
     Publisher  Bibtex source
  4. Kasmi, A., Laconte, J., Aufrère, R., Denis, D., & Chapuis, R. (2020). End-to-end probabilistic ego-vehicle localization framework. IEEE Transactions on Intelligent Vehicles.
     Bibtex source

Conference Articles

  1. Laconte, J., Randriamiarintsoa, E., Kasmi, A., Pomerleau, F., Chapuis, R., Debain, C., & Aufrère, R. (2021). Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS).
     Bibtex source
  2. Baril, D., Grondin, V., Deschenes, S., Laconte, J., Vaidis, M., Kubelka, V., Gallant, A., Giguere, P., & Pomerleau, F. (2020). Evaluation of Skid-Steering Kinematic Models for Subarctic Environments. 2020 17th Conference on Computer and Robot Vision (CRV), 198–205. https://doi.org/10.1109/CRV50864.2020.00034 Best Robotic Vision Paper Award!
     PDF Publisher  Bibtex source
  3. Kasmi, A., Laconte, J., Aufrère, R., Theodose, R., Denis, D., & Chapuis, R. (2020). An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap. 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 522–528.
     Bibtex source
  4. Laconte, J., Debain, C., Chapuis, R., Pomerleau, F., & Aufrere, R. (2019). Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS).
     Bibtex source
  5. Laconte, J., Deschênes, S.-P., Labussière, M., & Pomerleau, F. (2019). Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
     PDF  Bibtex source

Miscellaneous

  1. Vaidis, M., Laconte, J., Kubelka, V., & Pomerleau, F. (2020). Improving the Iterative Closest Point Algorithm using Lie Algebra. In IROS 2020 Workshop - Bringing geometric methods to robot learning, optimization and control.
     Bibtex source
  2. Labussière, M., Laconte, J., & Pomerleau, F. (2018). Geometry Preserving Sampling Method based on Spectral Decomposition for 3D Registration. In arXiv preprint arXiv:1810.01666.
     Bibtex source