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PhD defense of Louis SOUM-FONTEZ on “LiDAR-based domain generalization and unknown 3D object detection”

03/10/2025 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 Monday 10 March 2025 at Mines Paris – PSL.

Jury members:

NUCHTER Andreas, Professor at Wurtzbourg University – Rapporteur
CHECCHIN Paul, Professor at Clermont Auvergne University – Rapporteur
CHAMBON Sylvie, Professor at Institut de Recherche en Informatique de Toulouse (IRIT) – Examinatrice
GOULETTE François, Professor at ENSTA Paris – IP Paris – Thesis Director
DESCHAUD Jean-Emmanuel, Associate Professor at Mines Paris – PSL University – Thesis Co-Director

Summary of the PhD thesis:

The development of robust perception systems is fundamental for the safe and efficient operation of autonomous vehicles. These systems perform essential tasks such as 3D object detection, enabling vehicles to identify and localize obstacles, including other vehicles, pedestrians, and various objects within their environment. Accurate detection is critical for effective decision-making and navigation through complex driving scenarios. However, 3D object detection presents significant challenges due to the diverse nature of real-world conditions, which encompass a broad range of sensor setups, geographic environments, and scene complexities.

Firstly, this thesis work identifies and addresses the domain shifts that occur between different LiDAR datasets due to variations in sensor specifications, geographic environments, and dataset-specific attributes. These shifts often lead to significant performance gaps when models are transferred between datasets. To mitigate these issues, a multi-dataset training framework called MDT3D is introduced. MDT3D integrates data from various sources and employs novel augmentation and label harmonization techniques to create models that can generalize effectively across different conditions.

Secondly, the thesis presents ParisLuco3D, a dataset captured in urban areas around the Luxembourg Garden in Paris, designed to test model robustness in complex real-world scenarios. This provides a dedicated dataset to test model generalization, which we benchmark to highlight the poor performance of baseline generalization methods.

Lastly, the last axis of generalization explored is generalizing detection to novel unknown objects. We reframe the detection of unknown objects as an out-of-distribution (OOD) problem, allowing models to differentiate between known and previously unseen objects without compromising accuracy on familiar categories.

Congratulations to Louis !!!

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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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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.

PhD Defense: “Smart vehicule trajectory prediction in various autonomous driving scenarios”

04/20/2023 Ph.D. Defenses

Image showing autonomous cars on a roundabout with their trajectories

The Centre for Robotics is proud to announce that the PhD Defense of Thomas GILLES will take place on Friday, April 21st at MINES PARIS.

The subject of the work is:

“Smart vehicule trajectory prediction in various autonomous driving scenarios”

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

The PhD has been directed by Fabien Moutarde, and supervized by Bogdan Stanciulescu.

To attend:

MINES PARIS

60, Saint-Michel boulevard, Paris.

Jury members:

Alexandre ALAHI, Assistant Professor, EPFL

Fawzi NASHASHIBI, Research Director, National Institute for Research in Digital Science and Technology (Inria)

David FILLIAT, Full Professor, ENSTA Paris

Anne SPALANZANI, Full Professor, Université Grenoble Alpes (UGA) / National Institute for Research in Digital Science and Technology (Inria)

John FOLKESSON, Associate Professor, KTH Royal Institute of Technology, Sweden

Dzmitry TSISHKOU, PhD, Huawei Technologies France

Fabien MOUTARDE, Full Professor, MINES PARIS

Bogdan STANCIULESCU, Associate Professor, MINES PARIS

Abstract:

Recent advances in machine learning methods have enabled tremendous progress in autonomous driving, namely through the perception step thanks to deep learning and deep neural networks, combined with all-around progress in sensors, mapping and proprioception techniques. The focus is now therefore shifting towards the next steps in the autonomous pipeline, where prediction plays an important role. Once the surrounding road agents have been detected and tracked, the driving system needs to predict their future trajectory and plan accordingly to have a collision-less course.
This trajectory prediction must follow multiple requirements. First, it should obviously be accurate and trustworthy, so that its output can be reliably used in the following processes. The future can present multiple possibilities, from which it may not always be possible to disambiguate solely based on past historical data. The forecast must therefore be multimodal, by predicting multiple simultaneous probable futures. Since the prediction is to be made on all surrounding agents, and these agents behaviors are very much influenced by their interactions with each other, the model should take these interactions into account, and its multimodal predictions should be coherent with each other. Finally, for safety and reliability, the trajectory prediction should be easy to interpret, extensively evaluated, able to provide confidence evaluates and designed with its final use in the pipeline in mind.
In the first part of this dissertation, after recapitulating existing non-learning methods for trajectory forecasting, we study different existing representations and approaches for learning-based motion forecasting. We then propose to tackle the trajectory prediction problem using probability heatmaps to facilitate multimodality. We design three different ways of generating these heatmaps and evaluate them against each other and the existing state-of-the-art. We also provide a complete sampling method to extract actual trajectories from these heatmaps, and study the pro and cons of these heatmap methods compared to other commonly used frameworks. In the next chapter, we focus on multi-agent prediction, and more specifically consistent scene-level outputs, for these type of heatmap models through sampling and learned post-processing. Finally, we explore different ways of expanding prediction model evaluation by uncertainty assessment, calibration and cross-dataset generalizability analysis.