News

RECRUTEMENT D’UN ENSEIGNANT-CHERCHEUR (H/F) CONFIRMÉ EN ROBOTIQUE MOBILE

03/07/2024 Job

(deadline de candidature le 25 avril 2024)

Dans le cadre de l’ouverture prochaine d’une extension à Versailles-Satory, le Centre de Robotique de Mines-Paris PSL recrute un enseignant-chercheur (H/F) *confirmé* (≥10 ans après la thèse) permanent en Robotique mobile. Il/elle sera chargé-e de développer la recherche et les partenariats industriels spécifiquement pour les véhicules automatisés «off-road», notamment au sein de l’écosystème du plateau de Satory, centré sur des thématiques en lien avec la défense, avec des industriels tels que Nexter et Arquus, mais aussi potentiellement pour le BTP, pour l’agriculture, et pour l’intervention en milieu sinistré. Véhicule « off-road » est à comprendre d’abord au sens de voiture ou robot, ou engin de chantier terrestre tout terrain se déplaçant hors-route (chemin de terre, champs, forêt, zones détruites/abandonnées, chantier de BTP, …), mais pourrait aussi potentiellement concerner/impliquer des drones, ou robots marcheurs.
Les recherches devront comporter une composante de partenariat industriel, logiquement avec les acteurs du plateau de Satory, mais aussi avec d’autres comme Safran qui est déjà un partenaire important du centre. La thématique académique peut relever des algorithmes de Perception, et/ou ceux de Planification, et/ou ceux de Contrôle (voire la boucle robotique complète).

Le poste est un CDI de droit public de chargé ou directeur de recherche (selon expérience et détention ou non de l’HdR) de l’EPSCP MinesParis (ou peut, pour un-e candidat-e fonctionnaire de l’enseignement supérieur et recherche, être à terme un détachement dans un corps des enseignants de l’Institut Mines Telecom).

Profil
– Doctorat dans un domaine proche des algorithmes pour le véhicule intelligent ou la robotique mobile ;
– Expérience de Recherche d’au moins une dizaine d’années dans ces domaines ;
– Idéalement titulaire d’une Habilitation à Diriger des Recherche (HdR), ou prêt à la passer à très court terme

Cliquer ici pour la fiche de poste complète.

PhD defense of Jules Sanchez on “Domain generalization for semantic segmentation of LiDAR data for autonomous vehicles”

12/06/2023 Ph.D. Defenses

This thesis was realized under the direction of François Goulette and Jean-Emmanuel Deschaud of the Center of Robotics of Mines Paris – PSL.

The defense took place on Tuesday 5 December 2023 at Mines Paris – PSL.

Jury members:

LANDRIEU Loïc, Chargé de recherche, HDR, École des Ponts ParisTech
CHECCHIN Paul, Professeur des universités, Université Clermont Auvergne
CHAINE Raphaëlle, Professeur des universités, Université Claude Bernard Lyon 1, LIRIS
GOULETTE François, Professeur ENSTA Paris – IP Paris
DESCHAUD Jean-Emmanuel, Chargé de recherche HDR, Mines Paris – PSL

Summary of the PhD thesis:

LiDAR perception for autonomous vehicles managed to achieve suitable results on the various online benchmarks in the single-domain framework, that is to say when the training domain is the same as the evaluation domain. From there, the fields of research diversified and focused on questions of transferability, robustness and generalization.

This work focuses on generalization issues for LiDAR semantic segmentation. A global overview of the generalization performances, single-source and multi-source, of existing segmentation methods is carried out. To fairly perform these experiments, a dataset, ParisLuco3D, is created specifically to evaluate generalization.

Furthermore, a new single-source domain generalization method, 3DLabelProp, is proposed. This method differs from existing strategies by exploiting the geometry of the data to perform domain alignment rather than learning strategies. Beyond semantic segmentation, this method is also applied to the task of moving object segmentation.

Congratulations to Jules !!!

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PhD defense of Pierre Dellenbach on “Exploring LiDAR Odometries through Classical, Deep and Inertial perspectives”

11/10/2023 Ph.D. Defenses

This thesis was realized in collaboration between Mines Paris – PSL and Kitware under the direction of François Goulette and Jean-Emmanuel Deschaud of the Center of Robotics of Mines Paris – PSL, as well as Raphaël Cazorla from Kitware.

The defense took place on Friday 10 November 2023 at Mines Paris – PSL.

Summary of the PhD thesis: 3D LiDARs have become increasingly popular in the past decade, notably motivated by the safety requirements of autonomous driving requiring new sensor modalities. Contrary to cameras, 3D LiDARs provide direct, and extremely precise 3D measurements of the environment. This has led to the development of many different mapping and Simultaneous Localization And Mapping (SLAM) solutions leveraging this new modality. These algorithms quickly performed much better than their camera-based counterparts, as evidenced by several open-source benchmarks. One critical component of these systems is LiDAR odometry. A LiDAR odometry is an algorithm estimating the trajectory of the sensor, given only the iterative integration of the LiDAR measurements. The focus of this work is on the topic of LiDAR Odometries. More precisely, we aim to push the boundaries of LiDAR odometries, both in terms of precision and performance. To achieve this, we first explore classical LiDAR odometries in depth, and propose two novel LiDAR odometries, in chapter 3. We show the strength, and limitations of such methods. Then, to address to improve them we first investigate Deep Learning for LiDAR odometries in chapter 4, notably focusing on end-to-end odometries. We show again the limitations of such approaches and finally investigate in chapter 5 fusing inertial and LiDAR measurements.

Congratulations to Pierre !!!

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SAVE THE DATE – Monday, Sept. 18th at 10am: Seminar of Pr. Christian Gerdes, Stanford Uni.: “Racing towards the future of automated vehicles”

09/15/2023 Seminar

The Centre for Robotics is delighted to welcome J. Christian Gerdes, Professor Emeritus of Mechanical Engineering at Stanford University, for a talk at MINES PARIS on Monday, September 18th at 10:00:

“Racing towards the future of automated vehicles”

Many thanks to our Associate researcher Arnaud de La Fortelle from HEEX Technologies for making it happen.

You can attend in-person or remotely:

In-person: MINES PARIS, 60, Saint-Michel boulevard, Paris, room V106 B at 10:00 CET

By Zoom: 18/09/2023 at 10:00 CET : https://minesparis-psl-eu.zoom.us/j/95840587857?pwd=RXJOd0lIWGpCcDl5RTNEL2lhUkNiZz09 >
Meeting ID : 95840587857
Password : 313544

Abstract:

For over a century, automobile manufacturers have used the challenge of racing to better understand and improve upon vehicle design. Can the development of autonomous race cars advance the development of driver assistance systems and automated vehicles in a similar way? This talk explores our work with automated race cars at Stanford’s Dynamic Design Lab, identifying the basic challenges of racing and how control systems can handle these challenges. While automated vehicles hold significant advantages in computation and response time, head-to-head comparison with expert drivers shows humans can still teach the machine a few tricks. In closing this gap with the best human drivers, should engineers rely on the physics of motion to better model the car’s behavior, turn to AI to learn directly from data or attempt to blend these very different approaches? The talk concludes with a look at some of our latest experiments, the current state of the art and open questions on the road to the future.

PhD Defense: “Learned and Hybrid Strategies for Control and Planning of Highly Automated Vehicles”

09/15/2023 Ph.D. Defenses

The Centre for Robotics is proud to announce that the PhD Defense of Agapius BOU GHOSN will take place on Friday, September 15th at MINES PARIS.

The subject of the work is:

“Learned and Hybrid Strategies for Control and Planning of Highly Automated Vehicles“

The PhD has been directed by Arnaud de La Fortelle.

To attend:

MINES PARIS

60, Saint-Michel boulevard, Paris, room L109.

Jury members:

David FILLIAT, ENSTA Paris

Philippe MARTINET, INRIA Sophia-Antipolis

Christian GERDES, Stanford University

Brigitte D’ANDREA-NOVEL, Mines Paris

Philip POLACK, FAIRMAT

Arnaud DE LA FORTELLE, Heex Technologies

Abstract:

The thesis focuses on advancing both theoretically and experimentally toward a better understanding of hierarchical planning and control schemes and propose evaluation protocols for cooperative autonomous driving. Advances regarding more dynamic planning changes, including backup maneuvers that can require higher dynamics than usual driving are expected and also advances regarding learned planning and control schemes. All the contributions will be experimented on real cars.

PhD Defense: “Learning-based algorithms for real-time visual localization of mobile robots”

04/21/2023 Ph.D. Defenses

The Centre for Robotics is proud to announce that the PhD Defense of Arthur MOREAU will take place on Thursday, April 27th at MINES PARIS.

The subject of the work is:

“Learning-based algorithms for real-time visual localization of mobile robots”

This research is the result of a collaboration between our Centre and Huawei France.

The PhD has been directed by Arnaud de La Fortelle, and supervized by Bogdan Stanciulescu.

To attend:

MINES PARIS

60, Saint-Michel boulevard, Paris.

Jury members:

Valérie GOUET-BRUNET, Research Director, LASTIG – National Institute for Geographical and Forest Information (IGN)

Patric JANSFELT, Full Professor, KTH Royal Institute of Technology, Sweden

Vincent LEPETIT, Research Director, École des ponts ParisTech

Bogdan STANCIULESCU, Associate Professor, MINES PARIS

Arnaud de La Fortelle, HDR, CEO Heex Technologies

Dzmitry TSISHKOU, PhD, Huawei Technologies France

Abstract:

This PhD investigates visual-based algorithms for real-time localization of mobile robots. The core objective is to provide a reliable solution to the vehicle relocalization problem in large urban environments using camera images as input. Thanks to computer vision progress and low cost sensors, visual-based localization is an appealing solution, where the best performing methods use structure-based pipelines with high computational cost and memory footprint. On the other hand, learning-based approaches connect images and camera poses in an end-to-end fashion, matching real time embedded processing requirements. This PhD aims to push forward learning-based methods on several aspects: localization accuracy, uncertainty quantification, robustness to real-world conditions and easier adaptation to unseen maps. These limitations can be addressed in many ways: using Bayesian Deep Learning tools to provide uncertainty quantification of the model outputs; adapting the spatial and visual distribution of the training examples to increase robustness and reliability of the learned models; designing better model architecture to take in account geometrical clues within the learning process in order to reach state of the art accuracy for a tiny fraction of the computational cost of classical geometric pipelines. The resulting system is intended to be used as a scalable and reliable localization system for automated vehicles.