Alien Registration- Lajoie, Pierre (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/28618/thumbnail.jp

Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models

Perceptual aliasing is one of the main causes of failure for Simultaneous Localization and Mapping (SLAM) systems operating in the wild. Perceptual aliasing is the phenomenon where different places generate a similar visual (or, in general, perceptual) footprint. This causes spurious measurements to be fed to the SLAM estimator, which typically results in incorrect localization and mapping results. The problem is exacerbated by the fact that those outliers are highly correlated, in the sense that perceptual aliasing creates a large number of mutually-consistent outliers. Another issue stems from the fact that most state-of-the-art techniques rely on a given trajectory guess (e.g., from odometry) to discern between inliers and outliers and this makes the resulting pipeline brittle, since the accumulation of error may result in incorrect choices and recovery from failures is far from trivial. This work provides a unified framework to model perceptual aliasing in SLAM and provides practical algorithms that can cope with outliers without relying on any initial guess. We present two main contributions. The first is a Discrete-Continuous Graphical Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the standard SLAM problem, while the discrete portion describes the selection of the outliers and models their correlation. The second contribution is a semidefinite relaxation to perform inference in the DC-GM that returns estimates with provable sub-optimality guarantees. Experimental results on standard benchmarking datasets show that the proposed technique compares favorably with state-of-the-art methods while not relying on an initial guess for optimization.Comment: 13 pages, 14 figures, 1 tabl

Simultaneous Localization and Mapping Systems Robust to Perceptual Aliasing

De nos jours, la robotique gagne rapidement en popularité et promet un large éventail de nouvelles applications. Bien que le marché actuel soit dominé par les robots téléguidés, plusieurs compagnies cherchent à révolutionner notre quotidien avec des robots pleinement autonomes comme les voitures sans conducteur. En effet, les géants des technologies de partout dans le monde nous promettent régulièrement de nouvelles percées extraordinaires au niveau de l’autonomie des robots et multiplient des démonstrations plus impressionnantes les unes que les autres. Toutefois, ces systèmes autonomes devront se prouver extrêmement fiables et sécuritaires afin d’obtenir l’acceptabilité sociale nécessaire à leur succès. Malheureusement, les techniques présentement offertes par la littérature scientifique n’ont pas un niveau de robustesse à la hauteur des attentes de la population. C’est pourquoi les chercheurs universitaires et industriels doivent redoubler d’efforts afin de trouver de meilleures solutions qui sauront inspirer la confiance du public envers les systèmes robotiques autonomes. En particulier, une des composantes cruciales de tels systèmes est la localisation du robot dans son environnement. Cette composante est essentielle pour le déploiement de robots dans des environnements sans GPS (ex. à l’intérieur, sous terre, sous l’eau, etc.), puisque dans ces situations un robot doit estimer précisément sa position sur la seule base des mesures extraites à partir de ses propres senseurs. Pour y parvenir, une des techniques les plus populaires est la cartographie et localisation simultanée (SLAM) lors de laquelle un robot construit une carte de son environnement afin de suivre et estimer son propre mouvement et sa position. Cette technique est efficace, mais elle est tout de même vulnérable aux erreurs d’association et à la présence de mesures aberrantes. Les ingénieurs contournent généralement ce problème en performant une calibration très précise. Une telle calibration spécifique à l’environnement d’opération est appropriée pour des environnements très contrôlés comme ceux qu’on retrouve dans les laboratoires de recherche. Par contre, cette solution n’est pas viable pour des systèmes robotiques vendus au grand public et opérés par des utilisateurs sans formation. Une des principales causes d’erreurs en cartographie et localisation simultanée est l’aliasing perceptuel. Ce phénomène engendre des mesures aberrantes lorsqu’un robot confond deux endroits différents comme étant le même. L’addition de mesures aberrantes dans l’estimateur mène généralement à l’échec complet du système et donc possiblement à des conséquences dramatiques en termes de sécurité. Afin d’offrir des solutions à ces enjeux de robustesse, ce mémoire propose deux contributions à la littérature scientifique. La première introduit une nouvelle formulation pour le problème d’optimisation au coeur de la cartographie et localisation simultanée. Cette nouvelle formulation inclut un modèle explicite du phénomène d’aliasing perceptuel de façon à rejeter efficacement les mesures aberrantes. La seconde présente une nouvelle méthode de cartographie et localisation simultanée pour systèmes multi-robot qui est distribuée et robuste aux mesures aberrantes. Cette contribution est particulièrement importante puisque les systèmes multi-robots sont davantage vulnérables à l’aliasing perceptuel que les systèmes avec un seul robot. Plusieurs résultats expérimentaux obtenus lors de simulations, avec des jeux de données réelles et sur le terrain montrent que les techniques proposées produisent des estimés précis de localisation en présence de mesures aberrantes.----------ABSTRACT: Autonomous robotics is growing fast in popularity and has a large range of potential new applications. While the current market is dominated by human-controlled robots, many companies aim to revolutionize our daily lives by focusing on autonomous robotic platforms such as self-driving cars. Indeed, companies around the world regularly promise ground-breaking innovations and show very impressive demontrations of autonomous robots. However, to get the public acceptance they need to prosper, those autonomous systems have to be as safe and as reliable as possible. Unfortunately, the current implementations are not yet sufficiently robust, so academic and industrial researchers need to investigate better and more trustworthy solutions to the many challenges of autonomous navigation and behaviors. In particular, one of the most crucial components of most autonomous systems is the self-localization mechanism. This component is essential for the deployment of robots in GPS-denied environments (e.g. indoors, underground, submarine, etc.) since a robot would need to estimate is own position in its environment based on the measurements acquired by its own onboard sensors. In that regard, one of the most popular techniques is the simultaneous localization and mapping (SLAM) approach in which the robot builds a map of its surrounding environment to track and estimate its own movements and position. This technique has been proven to be very efficient, but it is also known as quite vulnerable to data association errors and the presence of spurious measurements. Engineers often circumvent those problems by doing a very precise, yet cumbersome, parameter tuning. Such environment-specific parameter tuning is appropriate for the controlled environment found in research laboratories, but it is by no means a sufficient solution for consumer robots deployed in the wild and sold to untrained customers. One of the main causes of errors in SLAM is the perceptual aliasing phenomenon in which two different places are confused as the same by the robot. This phenomenon leads to the addition of spurious measurements in the estimation mechanism which in turn leads to the failure of the whole system. In regard to the robustness challenges in SLAM systems, this thesis proposes two contributions to the scientific literature. The first introduces a new robust formulation of the core optimization problem in SLAM that models explicitly the perceptual aliasing phenomenon to efficiently reject spurious measurements. The second presents a distributed, online and robust solution for multi-robot SLAM in robotic teams. This contribution is particularly important since multi-robot systems are more vulnerable to perceptual aliasing than single-robot systems. Extensive experimental results in simulation, on datasets and on the field show that the proposed techniques can produce accurate localization estimates in the presence of spurious measurements

Évolution d'une ville: Rouen 1962-82

Pour une comparaison rigoureuse de l’espace rouennais, les auteurs proposent, au lieu du maillage changeant des îlots ou quartiers, un maillage fixe de carrés de 250 m de côté où les données sont transférées par calcul. Cette procédure («grid map» des Britanniques) autorise des traitements statistiques moins biaisés

Message Flow Analysis with Complex Causal Links for Distributed ROS 2 Systems

Distributed robotic systems rely heavily on the publish-subscribe communication paradigm and middleware frameworks that support it, such as the Robot Operating System (ROS), to efficiently implement modular computation graphs. The ROS 2 executor, a high-level task scheduler which handles ROS 2 messages, is a performance bottleneck. We extend ros2_tracing, a framework with instrumentation and tools for real-time tracing of ROS 2, with the analysis and visualization of the flow of messages across distributed ROS 2 systems. Our method detects one-to-many and many-to-many causal links between input and output messages, including indirect causal links through simple user-level annotations. We validate our method on both synthetic and real robotic systems, and demonstrate its low runtime overhead. Moreover, the underlying intermediate execution representation database can be further leveraged to extract additional metrics and high-level results. This can provide valuable timing and scheduling information to further study and improve the ROS 2 executor as well as optimize any ROS 2 system. The source code is available at: https://github.com/christophebedard/ros2-message-flow-analysis.Comment: 14 pages, 12 figure

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.Comment: 44 pages, 3 figure

Discrete fracture network based drift stability at the Éléonore mine

Photogrammetry tools were used to characterise the rock mass structural regime at selected mining drifts at the Éléonore underground mine in Canada. This information was used to provide the input data for generating a series of discrete fracture networks (DFN) models. The generated DFN models were subsequently used to investigate the creation of rock wedges along the drifts that may impact the stability of the excavations. The impact of the choice of employed DFN model on the analysis was investigated with reference to the stability of excavations. A series of parametric analyses demonstrated the sensitivity of the model to variations in the properties of the structural regime. The benefits of using stochastic modelling to capture the inherent variability are reviewed

Étude sur l’offre en matière du libre au Québec

This document presents in French the results of a survey carried out at the CIRANO by Pierre Lambert, student from HEC-Montréal. It analyzes the service offer of the Québec companies working with the free software. Ce document présente une enquête sur l'offre de service des entreprises œuvrant avec les logiciels libres. Elle a été réalisée au CIRANO par Pierre Lambert, étudiant à HEC Montréal, sous la supervision de M. Jean Talbot, professeur à HEC Montréal et de M. Robert Gérin-Lajoie, directeur exécutif du Groupe Commerce électronique du CIRANO. Elle a été commandée par Industrie Canada et vise à compléter une autre étude faite au cours de l'année 2003 : « Open Source Software au Canada, Le potentiel d'affaires des logiciels libres pour le secteur des technologies de l'information et des communications au Canada ». Ce document complète également un autre travail intitulé : « Vers un Réseau d’expertise en standards ouverts et en logiciels libres et ouverts ». Ce deuxième document est le résultat de plusieurs mois de travaux et de consultations entre plusieurs partenaires du Québec: le CRIM, le CIRANO, le RISQ, chercheurs universitaires, associations d'utilisateurs et entreprises du Québec. Une grande diversité d'expériences et d'initiatives existent au Québec autour du logiciel libre. Ces projets et initiatives locales, orientés jusqu'à maintenant sur des problèmes spécifiques, constituent une richesse grandement sous-estimée. Ces partenaires travaillent dans plusieurs projets, et depuis plusieurs années, à bâtir des solutions autour du libre. À partir de ce constat, nous proposons de regrouper les partenaires en un réseau d'expertise en standards ouverts et en logiciels libres.firms from Quebec, free software, open software, entreprises du Québec, logiciels libres, logiciels ouverts

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop closure candidates, which results in less accurate trajectory estimates. This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters. DOOR-SLAM is based on peer-to-peer communication and does not require full connectivity among the robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined with a distributed pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures; and a distributed SLAM front-end that detects inter-robot loop closures without exchanging raw sensor data. The system has been evaluated in simulations, benchmarking datasets, and field experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM produces more inter-robot loop closures, successfully rejects outliers, and results in accurate trajectory estimates, while requiring low communication bandwidth. Full source code is available at https://github.com/MISTLab/DOOR-SLAM.git.Comment: 8 pages, 11 figures, 2 table