Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

Paris-rue-Madame database: a 3D mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods

International audienceThis paper describes a publicly available 3D database from the rue Madame, a street in the 6th Parisian district. Data have been acquired by the Mobile Laser Scanning (MLS) system L3D2 and correspond to a 160 m long street section. Annotation has been carried out in a manually assisted way. An initial annotation is obtained using an automatic segmentation algorithm. Then, a manual refinement is done and a label is assigned to each segmented object. Finally, a class is also manually assigned to each object. Available classes include facades, ground, cars, motorcycles, pedestrians, traffic signs, among others. The result is a list of (X, Y, Z, reflectance, label, class) points. Our aim is to offer, to the scientific community, a 3D manually labeled dataset for detection, segmentation and classification benchmarking. With respect to other databases available in the state of the art, this dataset has been exhaustively annotated in order to include all available objects and to allow point-wise comparison

MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization

Supervised 3D Object Detection models have been displaying increasingly better performance in single-domain cases where the training data comes from the same environment and sensor as the testing data. However, in real-world scenarios data from the target domain may not be available for finetuning or for domain adaptation methods. Indeed, 3D object detection models trained on a source dataset with a specific point distribution have shown difficulties in generalizing to unseen datasets. Therefore, we decided to leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method to increase the robustness of 3D object detection models when tested in a new environment with a different sensor configuration. To tackle the labelling gap between datasets, we used a new label mapping based on coarse labels. Furthermore, we show how we managed the mix of datasets during training and finally introduce a new cross-dataset augmentation method: cross-dataset object injection. We demonstrate that this training paradigm shows improvements for different types of 3D object detection models. The source code and additional results for this research project will be publicly available on GitHub for interested parties to access and utilize: https://github.com/LouisSF/MDT3DComment: Accepted for publication at IROS 202

Domain generalization of 3D semantic segmentation in autonomous driving

Using deep learning, 3D autonomous driving semantic segmentation has become a well-studied subject, with methods that can reach very high performance. Nonetheless, because of the limited size of the training datasets, these models cannot see every type of object and scene found in real-world applications. The ability to be reliable in these various unknown environments is called \textup{domain generalization}. Despite its importance, domain generalization is relatively unexplored in the case of 3D autonomous driving semantic segmentation. To fill this gap, this paper presents the first benchmark for this application by testing state-of-the-art methods and discussing the difficulty of tackling Laser Imaging Detection and Ranging (LiDAR) domain shifts. We also propose the first method designed to address this domain generalization, which we call 3DLabelProp. This method relies on leveraging the geometry and sequentiality of the LiDAR data to enhance its generalization performances by working on partially accumulated point clouds. It reaches a mean Intersection over Union (mIoU) of 50.4% on SemanticPOSS and of 55.2% on PandaSet solid-state LiDAR while being trained only on SemanticKITTI, making it the state-of-the-art method for generalization (+5% and +33% better, respectively, than the second best method). The code for this method is available on GitHub: https://github.com/JulesSanchez/3DLabelProp

Interaction d'un rayonnement X-XUV intense avec la matière : cinétique atomique associée

This work follows the recent development of the free electron lasers in the X-ray and XUV-ray range (XFEL). Unlike optical sources that deposit their energy via the free electrons, the X/XUV photons deposit their energy directly via photoionization of inner shell electrons with the ejection of photo-electrons, followed by the ejection of Auger electrons and three body recombination electrons in the free electron distribution. The matter is thus heated via the atomic structure. The high XFEL intensity allows one to make up to one hole per atom in a solid, thus producing, on a femtosecond time scale, an exotic state, highly out of equilibrium, called hollow cristal. This unstable exotic state deexcite via a set of elementary atomic processes. In this work we were interested in the development of tools to calculate the atomic population kinetics, coupled to the free electron kinetics, during the transition at constant ionic density, from solid to dense plasma, induced by an XFEL irradiating a solid target. The goal here was to calculate this out of equilibrium coupled kinetics between states of matter having a very different nature. To address this problem we have developed two kinetics models of XFEL interaction with solids. In both these models the description of the solid as a cold degenerated plasma allowed us to use the same plasma approach during all the solid-plasma transition. Considering the fact that all the atomic physics takes place at solid density, way before the matter relaxation, we have developed two codes, associated with these two models, for a use at constant ionic density. To approach this study, we first focused on the bound electron kinetics assuming a free electron distribution at equilibrium (i.e. hypothesis of instantaneous thermalization of the free electrons). In the framework of the dense plasma approach extended up to the solid state, we have developed a generalized collisional radiative model. This generalization goes through the identification of a link between the solid state and the plasma state for the elementary atomic processes. The code associated with this model allowed us to study experimental results and to improve our description of the density effects in dense plasmas. In a second part the free electron kinetics is included in the model with a free electron distribution out of thermodynamic equilibrium. The associated code, based on the discretization of this distribution and its coupling with bound atomic states allowed us to study the role of the atomic elementary processes in the free electron distribution thermalization.Ce travail de thèse suit l'apparition récente de ces nouvelles sources intenses et courtes de rayonnement dans la gamme X/XUV que sont les lasers X/XUV à électrons libres (XFEL). Contrairement aux sources optiques qui déposent principalement leur énergie via les électrons libres, les photons X/XUV déposent leur énergie dans la matière par la photoionisation de couches internes avec éjection de photo-électrons, suivie par l'éjection d'électrons Auger et d'électrons de recombinaison à trois corps dans la distribution d'électrons libres. Le chauffage se fait donc par l'intermédiaire de la structure atomique. La forte intensité des XFELs permet de faire jusqu'à un trou par atome dans un solide produisant ainsi, sur une échelle femtoseconde, un état exotique fortement hors-équilibre appelé solide creux. Cet état exotique instable se désexcite via un ensemble de processus atomiques élémentaires. Nous nous sommes intéressés dans cette thèse au développement d'outils permettant de calculer la cinétique des populations atomiques, couplée à la cinétique des électrons libres, pendant la transition à densité ionique constante, de solide à plasma dense en passant par l'état de solide creux, induit par le rayonnement XFEL irradiant une cible solide. Tout le défi ici a été de calculer cette cinétique couplée hors-équilibre entre ces états de la matière de nature très différente. Pour répondre a ce défi nous avons développé deux modèles cinétiques d'interaction XFELsolide, pour lesquels la description d'un solide comme un plasma froid dégénéré nous a permis d'utiliser une même approche plasma pendant l'ensemble de la transition du solide au plasma. L'ensemble de la physique atomique HETL d'intérêt ayant lieu à densité du solide, bien avant la détente de la matière, nous avons développé deux codes associés à ces modèles pour une utilisation à densité ionique constante. Pour aborder l'étude nous nous sommes d'abord concentrés sur la cinétique des électrons liés en supposant une distribution d'électrons libres à l'équilibre (ce qui suppose une thermalisation instantanée des électrons libres). Dans le cadre de l'approche de plasma dense étendue jusqu'au solide, nous avons développé un modèle collisionnel-radiatif généralisé. Cette généralisation passe par l'identification d'un lien entre état solide et plasma au niveau des processus atomiques élémentaires. Le code développé à partir de ce modèle nous a permis d'étudier des résultats expérimentaux et ainsi d'améliorer notre description des effets de densités dans les plasmas denses. Dans une seconde partie nous avons ajouté à l'étude la cinétique des électrons libres en considérant une distribution d'électrons libres hors-équilibre. Le code associé, basé sur la discrétisation de cette distribution et son couplage avec les états liés, nous a permis d'étudier le rôle des processus atomiques élémentaires dans la thermalisation de la distribution d'électrons libres

ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception

LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. %Exploiting this information for perception is interesting as the amount of available data increases. As the performance of various LiDAR perception tasks has improved, generalizations to new environments and sensors has emerged to test these optimized models in real-world conditions. This paper provides a novel dataset, ParisLuco3D, specifically designed for cross-domain evaluation to make it easier to evaluate the performance utilizing various source datasets. Alongside the dataset, online benchmarks for LiDAR semantic segmentation, LiDAR object detection, and LiDAR tracking are provided to ensure a fair comparison across methods. The ParisLuco3D dataset, evaluation scripts, and links to benchmarks can be found at the following website:https://npm3d.fr/parisluco3

AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-time High-Fidelity LiDAR Simulation

LiDAR sensors provide rich 3D information about their surrounding and are becoming increasingly important for autonomous vehicles tasks, such as semantic segmentation, object detection, and tracking. Simulating a LiDAR sensor accelerates the testing, validation, and deployment of autonomous vehicles, while reducing the cost and eliminating the risks of testing in real-world scenarios. We address the problem of high-fidelity LiDAR simulation and present a pipeline that leverages real-world point clouds acquired by mobile mapping systems. Point-based geometry representations, more specifically splats, have proven their ability to accurately model the underlying surface in very large point clouds. We introduce an adaptive splats generation method that accurately models the underlying 3D geometry, especially for thin structures. Moreover, we introduce a physics-based, faster-than-real-time LiDAR simulator, in the splatted model, leveraging the GPU parallel architecture with an acceleration structure, while focusing on efficiently handling large point clouds. We test our LiDAR simulation in real-world conditions, showing qualitative and quantitative results compared to basic splatting and meshing techniques, demonstrating the interest of our modeling technique.Comment: 28 pages, 11 figures, 6 table

Efficient calculation of degenerate atomic rates by numerical quadrature on GPUs

The rates of atomic processes in cold, dense plasmas are governed strongly by effects of quantum degeneracy. The electrons follow Fermi-Dirac statis- tics and their high density limits the number of quantum states available for occupation after a collision. These factors preclude a direct solution to the usual rate coefficient integrals. We summarise the formulation of this problem and present a simple, but efficient method of evaluating collisional rate coefficients via direct numerical integration. Numerical quadrature has an intrinsically high level of parallelism, ideally suited for graphics processor units. GPUs are particularly suited to this problem because of the large number of integrals which must be carried out simultaneously for a given atomic model. A CUDA code to calculate the rates of signicant atomic processes as part of a collisional-radiative model is presented and discussed. This approach may be readily extended to other applications where rapid and repeated evaluation of many integrals is required