18 research outputs found

    Localisation d'un véhicule autonome à partir d'une carte a priori de points 3D

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    The concept of self-driving vehicles is becoming a happening reality and will soon share our roads with other vehicles –autonomous or not-. For a self-driving car to move around in its environment in a securely, it needs to sense to its immediate environment and most importantly localize itself to be able to plan a safe trajectory to follow. Therefore, to perform tasks suchas trajectory planning and navigation, a precise localization is of upmost importance. This would further allow the vehicle toconstantly plan and predict an optimal path in order to weave through cluttered spaces by avoiding collisions with other agentssharing the same space as the latter. For years, the Global Positioning System (GPS) has been a widespread complementary solution for navigation. The latter allows only a limited precision (range of several meters). Although the Differential GPSand the Real Time Kinematic (RTK) systems have reached considerable accuracy, these systems remain sensitive to signal masking and multiple reflections, offering poor reliability in dense urban areas. All these deficiencies make these systems simply unsuitable to handle hard real time constraints such as collision avoidance. A prevailing alternative that has attracted interest recently, is to use upload a prior map in the system so that the agent can have a reliable support to lean on. Indeed,maps facilitate the navigation process and add an extra layer of security and other dimensions of semantic understanding. The vehicle uses its onboard sensors to compare what it perceives at a given instant to what is stored in the backend memory ofthe system. In this way, the autonomous vehicle can actually anticipate and predict its actions accordingly.The purpose of this thesis is to develop tools allowing an accurate localization task in order to deal with some complex navigation tasks outlined above. Localization is mainly performed by matching a 3D prior map with incoming point cloudstructures as the vehicle moves. Three main objectives are set out leading with two distinct phases deployed (the map building and the localization). The first allows the construction of the map, with centimeter accuracy using static or dynamic laser surveying technique. Explicit details about the experimental setup and data acquisition campaigns thoroughly carried outduring the course of this work are given. The idea is to construct efficient maps liable to be updated in the long run so thatthe environment representation contained in the 3D models are compact and robust. Moreover, map-building invariant on any dedicated infrastructure is of the paramount importance of this work in order to rhyme with the concept of flexible mapping and localization. In order to build maps incrementally, we rely on a self-implementation of state of the art iterative closest point (ICP) algorithm, which is then upgraded with new variants and compared to other implemented versions available inthe literature. However, obtaining accurate maps requires very dense point clouds, which make them inefficient for real-time use. Inthis context, the second objective deals with points cloud reduction. The proposed approach is based on the use of both colorinformation and the geometry of the scene. It aims to find sets of 3D points with the same color in a very small region and replacing each set with one point. As a result, the volume of the map will be significantly reduced, while the proprieties of this map such as the shape and color of scanned objects remain preserved.The third objective resort to efficient, precise and reliable localization once the maps are built and treated. For this purpose, the online data should be accurate, fast with low computational effort whilst maintaining a coherent model of the explored space. To this end, the Velodyne HDL-32 comes into play. (...)Les véhicules autonomes, qualifiés aussi de véhicules sans conducteur, deviennent dans certains contextes une réalité tangible et partageront très bientôt nos routes avec d’autres véhicules classiques. Pour qu’un véhicule autonome se déplace de manière sécurisée, il doit savoir où il se trouve et ce qui l’entoure dans l’environnement. Pour la première tâche, pour déterminer sa position dans l’environnement, il doit se localiser selon six degrés de liberté (position et angles de rotation). Alors que pour la deuxième tâche, une bonne connaissance de cet environnement « proche » est nécessaire, ce qui donne lieu à une solution sous forme de cartographie. Par conséquent, pour atteindre le niveau de sécurité souhaité des véhicules autonomes, une localisation précise est primordiale. Cette localisation précise permet au véhicule non seulement de se positionner avec précision, mais également de trouver sa trajectoire optimale et d’éviter efficacement les collisions avec des objets statiques et dynamiques sur son trajet. Actuellement, la solution la plus répandue est le système de positionnement (GPS). Ce système ne permet qu’une précision limitée (de l’ordre de plusieurs mètres) et bien que les systèmes RTK (RealTime Kinematic) et DGPS (Differential GPS) aient atteint une précision bien plus satisfaisante, ces systèmes restent sensibles au masquage des signaux, et aux réflexions multiples, en particulier dans les zones urbaines denses. Toutes ces déficiences rendent ces systèmes inadaptés pour traiter des tâches critiques telles que l’évitement des collisions. Une alternative qui a récemment attiré l’attention des experts (chercheurs et industriels), consiste à utiliser une carte à priori pour localiser la voiture de l’intérieur de celui-ci. En effet, les cartes facilitent le processus de navigation et ajoutent une couche supplémentaire de sécurité et de compréhension. Le véhicule utilise ses capteurs embarqués pour comparer ce qu’il perçoit à un moment donné avec ce qui est stocké dans sa mémoire. Les cartes à priori permettent donc au véhicule de mieux se localiser dans son environnement en lui permettant de focaliser ses capteurs et la puissance de calcul uniquement sur les objets en mouvement. De cette façon, le véhicule peut prédire ce qui devrait arriver et voir ensuite ce qui se passe réellement en temps réel, et donc peut prendre une décision sur ce qu’il faut faire.Cette thèse vise donc à développer des outils permettant une localisation précise d’un véhicule autonome dans un environnement connu à priori. Cette localisation est déterminée par appariement (Map-matching) entre une carte de l’environnement disponible a priori et les données collectées au fur et à mesure que le véhicule se déplace. Pour ce faire, deux phases distinctes sont déployées. La première permet la construction de la carte, avec une précision centimétrique en utilisant des techniques de construction de cartes statiques ou dynamiques. La seconde correspond à la capacité de localiser le véhicule dans cette carte 3D en l’absence d’infrastructures dédiées comprenant le système GPS, les mesures inertielles (IMU) ou des balises.Au cours de ce travail, différentes techniques sont développées pour permettre la réalisation des deux phases mentionnées ci-dessus. Ainsi, la phase de construction de cartes, qui consiste à recaler des nuages de points capturés pour construire une représentation unique et unifiée de l’environnement, correspond au problème de la localisation et de la cartographie simultanée (SLAM). Afin de faire face à ce problème, nous avons testé et comparé différentes méthodes de recalage. Cependant, l’obtention de cartes précises nécessite des nuages de points très denses, ce qui les rend inefficaces pour une utilisation en temps réel. Dans ce contexte, une nouvelle méthode de réduction des points est proposée. (...

    Color-based 3D point cloud reduction

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    International audienc

    CICP: Cluster Iterative Closest Point for Sparse-Dense Point Cloud Registration

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    International audiencePoint cloud registration is an important and fundamental building block of mobile robotics. It forms an integral part of the processes of mapping, localization, object detection and recognition, loop closure and many other applications. Throughout the years, registration has been addressed in different ways, based on local features, global descriptor or object-based. However, all these techniques give meaningful results only if the input data are of the same type and density (resolution). Recently, with the technological revolution of 3D sensors, accurate ones producing dense clouds have appeared as well as others faster, more compatible with real-time applications, producing sparse clouds. Accuracy and speed are two sought-after concepts in every robotic application including those cited above, which involves the simultaneous use of both types of sensors, resulting in sparse-dense (or dense-sparse) point cloud registration. The difficulty of sparse to dense registration lies in the fact that there is no direct correspondence between each point in the two clouds, but rather a point equivalent to a set of points. In this paper, a novel approach that surpasses the notion of density is proposed. Its main idea consists in matching points representing each local surface of source cloud with the points representing the corresponding local surfaces in the target cloud. Experiments and comparisons with state-of-the-art methods show that our approach gives better performance. It handles registration of point clouds of different densities acquired by the same sensor with varied resolution or taken from different sensors

    Capsular typing of Streptococcus pneumoniae isolated in an Algerian hospital using a new multiplex PCR-based scheme

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    We developed a new sequential multiplex-PCR-based typing scheme (MPBTS) for pneumococcal capsular classification. The serogroup/type of 37 control isolates obtained by the Quellung reaction, MPBTS, and nucleotide sequencing, were fully concordant. The serogroups/types of 75 invasive isolates determined by MPBTS, presented 100% specificity and 96% sensitivity, when compared with the Quellung reaction.H. Ziane was supported by a grant from Ministère de l'Enseignement Superieur et de la Recherche Scientifique in Algeria. V. Manageiro was supported by the grant SFRH/BPD/77486/2011, from Fundação para a Ciência e a Tecnologia, Lisbon, Portugal

    Les syndromes myasthéniques congénitaux avec anomalies cinétiques du récepteur à l’acétylcholine

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    Les syndromes myasthéniques congénitaux (SMC) sont des affections génétiquement et phénotypiquement très hétérogènes responsables d’un déficit de la transmission neuromusculaire. Les formes dites post-synaptiques sont les plus fréquentes des SMC, et parmi elles, le déficit en récepteur à l’acétylcholine (low expressor) est le mécanisme physiopathologique le plus souvent en cause. Les SMC avec anomalies cinétiques du récepteur à l’acétylcholine sont beaucoup plus rares et à l’origine de tableaux cliniques à l’issue parfois dramatique. On en dénombre deux types : le syndrome du canal lent et le syndrome du canal rapide. Leur diagnostic et leur prise en charge thérapeutique sont spécifiques à chaque type. Dans ce travail, nous détaillerons leurs aspects phénotypiques respectifs en les illustrant par les observations de trois familles algériennes

    CTX-M-3 and CTX-M-15 Extended-Spectrum β-Lactamases in Isolates of Escherichia coli from a Hospital in Algiers, Algeria

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    Sixteen strains of Escherichia coli isolated between January and June 2005 in a hospital in Algiers carry the ISEcp1 element and the TEM and either CTX-M-3 (n = 3) or CTX-M-15 (n = 13) β-lactamases. Fourteen of the isolates are multidrug resistant. Five isolates from the neonatal ward were indistinguishable by pulsed-field gel electrophoresis

    La grande variabilité phénotypique des mutations du gène

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    Le gène RYR1 (Ryanodine-Receptor-1) code pour une protéine-clé dans le processus de couplage excitation-contraction de la fibre musculaire. Ce récepteur est le principal canal de libération du calcium à partir du réticulum endoplasmique [

    Role of SHV β-lactamase variants in resistance of clinical Klebsiella pneumoniae strains to β-lactams in an Algerian hospital

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    Three clinical Klebsiella pneumoniae strains, KpARG74, KpARG220 and KpARG185, isolated from a hospital in Algeria, carried the novel β-lactamases SHV-98, SHV-99 and SHV-100, respectively, and co-expressed TEM-1 and either CTX-M-3 or CTX-M-15. In contrast, transformed cells possessing the genes for these novel β-lactamases, i.e. EcDH5α-SHV-98, EcDH5α-SHV-99 and EcDH5α-SHV-100, respectively, carried unique sequence features of blaSHV gene variants, enabling oxyimino-cephalosporin susceptibility and confirming that none of the transformants exhibited extended-spectrum β-lactamase (ESBL) properties. SHV-100 is apparently functional, despite differing from the SHV-1 sequence by duplication of 13 amino acids. The SHV-99 enzyme differed from the parental SHV-1 by the amino acid substitution Asp104→Gly, which is an important position in the development of the ESBL phenotype in TEM β-lactamases. This is the first time, to our knowledge, that this mutation has been reported in clinically occurring isolates. Thus, kinetic characterization of the SHV-99 enzyme was performed. The SHV-99 enzyme showed higher affinity (Km of 196 µM), catalytic activity (kcat of 0.5 s−1) and catalytic efficiency (kcat/Km of 0.003 µM−1 s−1) than SHV-1 β-lactamase against aztreonam. These results showed that the neutral glycine at residue 104 increased the affinity of the enzyme to aztreonam, but was unable to develop the ESBL phenotype in SHV enzymes. As the emergence of new threatening combinations of resistance determinants among nosocomial pathogens is further possible, this study has highlighted the need to reverse the spread of initial mutations

    Detection of Methicillin-Resistant Staphylococcus aureus Strains Resistant to Multiple Antibiotics and Carrying the Panton-Valentine Leukocidin Genes in an Algiers Hospital

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    Forty-five Panton-Valentine leukocidin (PVL)-positive, methicillin-resistant Staphylococcus aureus strains were isolated in Algeria between 2003 and 2004; 18 isolates were isolated in the community and 27 in a hospital. Five PVL-positive hospital isolates were resistant to multiple antibiotics, including ofloxacin and gentamicin for three isolates
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