Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system

Informative Path Planning for Active Field Mapping under Localization Uncertainty

Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on any manually tuned parameters. Extensive simulations show that our approach outperforms existing strategies, with reductions in mean pose uncertainty and map error. We also present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation Letters (and IEEE International Conference on Robotics and Automation

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

On the Uncertainty in Active SLAM: Representation, Propagation and Monotonicity

La localización y mapeo simultáneo activo (SLAM activo) ha recibido mucha atención por parte de la comunidad de robótica por su relevancia en aplicaciones de robot móviles. El objetivo de un algoritmo de SLAM activo es planificar la trayectoria del robot para maximizar el área explorada y minimizar la incertidumbre asociada con la estimación de la posición del robot. Durante la fase de exploración de un algoritmo de SLAM, donde el robot navega en una región previamente desconocida, la incertidumbre asociada con la localización del robot crece sin límites. Solo después de volver a visitar regiones previamente conocidas, se espera una reducción en la incertidumbre asociada con la localización del robot mediante la detección de cierres de bucle. Esta tesis doctoral se centra en la importancia de representar y cuantificar la incertidumbre para calcular correctamente la confianza asociada con la estimación de la localización del robot en cada paso de tiempo a lo largo de su recorrido y, por lo tanto, decidir la trayectoria correcta de acuerdo con el objetivo de SLAM activo.En la literatura, se han propuesto fundamentalemente dos tipos de modelos de representación de la incertidumbre: absoluta y diferencial. En representación absoluta, la información sobre la incertidumbre asociada con la localización del robot está representada por una función de distribución de probabilidad, generalmente gausiana, sobre las variables de localización absoluta con respecto a una referencia base elegida. La estimación de la posición del robot está dada por la esperanza de las variables asociadas con la localización y la incertidumbre por su matriz de covarianza asociada. La representación diferencial utiliza una representación local de la incertidumbre, la posición estimada del robot se representa mediante la mejor aproximación de la posición absoluta y el error de estimación se representa localmente mediante un vector diferencial. Este vector generalmente también está representado por una función de distribución de probabilidad gausiana. Representaciones equivalentes al modelo diferencial han utilizado las herramientas de Grupos de Lie y Álgebras de Lie para representar la incertidumbre. Además de estos modelos, existen diferentes formas de representar la posición y orientación de la posición del robot, ángulos de Euler, cuaterniones y transformaciones homogéneas.Los enfoques más comunes para cuantificar la incertidumbre en SLAM se basan en criterios de optimalidad con el objetivo de cuantificar el mapa y la incertidumbre de la posición del robot: A-opt (traza de la matriz de covarianza, o suma de sus autovalores), D-opt (determinante de la matriz de covarianza, o producto de sus autovalores) y E-opt (criterio del mayor autovalor). Alternativamente, otros algoritmos de SLAM activo, basados en la Teoría de la Información, se basan en el uso de la entropía de Shannon para seleccionar acciones que lleven al robot al objetivo seleccionado. En un escenario de SLAM activo, garantizar la monotonicidad de estos criterios en la toma de decisiones durante la exploración, es decir, cuantificar correctamente que la incertidumbre encapsulada en una matriz de covarianza está aumentando, es un paso esencial para tomar decisiones correctas. Como ya se ha mencionado, durante la fase de exploración la incertidumbre asociada con la localización del robot aumenta. Por lo tanto, si no se preserva la monotonicidad de los criterios considerados, el sistema puede seleccionar trayectorias o caminos que creen falsamente que conducen a una menor incertidumbre de la localización del robot.En esta tesis, revisamos el trabajo relacionado sobre representación y propagación de la incertidumbre de la posición del robot en los diferentes modelos propuestos en la literatura. Además, se lleva a cabo un análisis de la incertidumbre representada localmente con un vector diferencial y la incertidumbre representada usando grupos de Lie. Investigamos la monotonicidad de diferentes criterios para la toma de decisiones, tanto en 2D como en 3D, dependiendo de la representación de la incertidumbre y de la representación de la orientación del robot. Nuestra conclusión fundamental es que la representación de la incertidumbre sobre grupos de Lie y usando un vector diferencial son similares e independientes de la representación utilizada para la parte rotacional de la posición del robot. Esto se debe a que la incertidumbre se representa localmente en el espacio de las transformaciones diferenciales que se corresponde con el álgebra de Lie del grupo euclidiano especial SE(n). Sin embargo, en el espacio tridimensional, la estimación de la localización del robot depende de las diferentes formas de representación de la parte rotacional. Por lo tanto, una forma adecuada de manipular conjuntamente la estimación y la incertidumbre del robot es utilizando la teoría de grupos de Lie debido a que es una representación que garantiza propiedades tales como una representación mínima y libre de singularidades en los ángulos de rotación. Analíticamente, demostramos que, utilizando representaciones diferenciales para la propagación de la incertidumbre, la monotonicidad se conserva para todos los criterios de optimalidad, A-opt, D-opt y E-opt y para la entropía de Shannon. También demostramos que la monotonicidad no se cumple para ninguno de ellos en representaciones absolutas usando ángulos Roll-Pitch-Yaw y Euler. Finalmente, mostramos que al usar cuaterniones unitarios en representaciones absolutas, los únicos criterios que preservan la monotonicidad son D-opt y la entropía de Shannon.Estos hallazgos pueden guiar a los investigadores de SLAM activo a seleccionar adecuadamente un modelo de representación de la incertidumbre, de modo que la planificación de trayectorias y los algoritmos de exploración puedan evaluar correctamente la evolución de la incertidumbre asociada a la posición del robot.Active Simultaneous Localization and Mapping (Active SLAM) has received a lot of attention from the robotics community for its relevance in mobile robotics applications. The objective of an active SLAM algorithm is to plan ahead the robot motion in order to maximize the area explored and minimize the uncertainty associated with the estimation, all within a time and computation budget. During the exploration phase of a SLAM algorithm, where the robot navigates in a previously unknown region, the uncertainty associated with the robot's localization grows unbounded. Only after revisiting previously known regions a reduction in the robot's localization uncertainty is expected by detecting loop-closures. This doctoral thesis focuses on the paramount importance of representing and quantifying uncertainty to correctly report the associated confidence of the robot's location estimate at each time step along its trajectory and therefore deciding the correct course of action in an active SLAM mission. Two fundamental types of models of probabilistic representation of the uncertainty have been proposed in the literature: absolute and dfferential. In absolute representations, the information about the uncertainty in the location of the robot's pose is represented by a probability distribution function, usually Gaussian, over the variables of the absolute location with respect to a chosen base reference. The estimated location is given by the expected location variables and the uncertainty by its associated covariance matrix. Differential representations use a local representation of the uncertainty, the estimated location of the robot is represented by the best approximation of the absolute location and the estimation error is represented locally by a differential location vector. This vector is usually also represented by a Gaussian probability distribution function. Equivalent representations to differential models have used the tools of Lie groups and Lie algebras to represent uncertainties. In addition to uncertainty models, there are different ways to represent the position and orientation of the robot's pose, Euler angles, quaternions and homogeneous transformations. The most common approaches to quantifying uncertainty in SLAM are based on optimality criteria which aim at quantifying the map and robot's pose uncertainty, namely A-opt (trace of the covariance matrix, or sum of its eigenvalues), D-opt (determinant of the covariance matrix, or product of its eigenvalues) and E-opt (largest eigenvalue) criteria. Alternatively, other active SLAM algorithms, based on Information Theory, rely on the use of the Shannon's entropy to select courses of action for the robot to reach the commanded goal location. In an active SLAM scenario, guaranteeing monotonicity of these decision making criteria during exploration, i.e. quantifying correctly that the uncertainty encapsulated in a covariance matrix is increasing, is an essential step towards making correct decisions. As already mentioned, during exploration the uncertainty associated with the robot's localization increases. Therefore, if monotonicity of the criteria considered is not preserved, the system might select courses of action or paths that it falsely believes lead to less uncertainty in the robot. In this thesis, we review related work about representation and propagation of the uncertainty of robot's pose and present a survey of different types of models proposed in the literature. Additionally, an analysis of the uncertainty represented with a differential uncertainty vector and the uncertainty represented on Lie groups is carried out. We investigate the monotonicity of different decision making criteria, both in 2D and 3D, depending on the representation of uncertainty and the orientation of the robot's pose. Our fundamental conclusion is that uncertainty representation over Lie groups and using differential location vectors are similar and independent of the representation used for rotational part of the robot's pose. This is due to the uncertainty is represented locally in the space of differential transformations for translation and rotation that correspond with the Lie algebra of special Euclidean group SE(n). However, in 3-dimensional space, the homogeneous transformation associated to the approximation of the real location depend on the different ways of representation the rotational part. Therefore, a proper way to jointly manipulating the estimation and uncertainty of the pose is to use the theory of Lie groups due to it is a representation to guarantee properties such as a minimal representation and free of singularities in rotation angles. We analytically show that, using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, A-opt, D-opt and E-opt and for Shannon's entropy. We also show that monotonicity does not hold for any of them in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, we show that using unit quaternions in absolute representations, the only criteria that preserve monotonicity are D-opt and Shannon's entropy. These findings can guide active SLAM researchers to adequately select a representation model for uncertainty, so that path planning and exploration algorithms can correctly assess the evolution of location uncertainty.<br /

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

Semantic information for robot navigation: a survey

There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situationsThe research leading to these results has received funding from HEROITEA: Heterogeneous 480 Intelligent Multi-Robot Team for Assistance of Elderly People (RTI2018-095599-B-C21), funded by Spanish 481 Ministerio de Economía y Competitividad. The research leading to this work was also supported project "Robots sociales para estimulacón física, cognitiva y afectiva de mayores"; funded by the Spanish State Research Agency under grant 2019/00428/001. It is also funded by WASP-AI Sweden; and by Spanish project Robotic-Based Well-Being Monitoring and Coaching for Elderly People during Daily Life Activities (RTI2018-095599-A-C22)

A survey on active simultaneous localization and mapping: state of the art and new frontiers

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics

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

Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd