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

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Robust Incremental Smoothing and Mapping (riSAM)

This paper presents a method for robust optimization for online incremental Simultaneous Localization and Mapping (SLAM). Due to the NP-Hardness of data association in the presence of perceptual aliasing, tractable (approximate) approaches to data association will produce erroneous measurements. We require SLAM back-ends that can converge to accurate solutions in the presence of outlier measurements while meeting online efficiency constraints. Existing robust SLAM methods either remain sensitive to outliers, become increasingly sensitive to initialization, or fail to provide online efficiency. We present the robust incremental Smoothing and Mapping (riSAM) algorithm, a robust back-end optimizer for incremental SLAM based on Graduated Non-Convexity. We demonstrate on benchmarking datasets that our algorithm achieves online efficiency, outperforms existing online approaches, and matches or improves the performance of existing offline methods.Comment: Accepted to ICRA 202

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

Localization from semantic observations via the matrix permanent

Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization