2,230 research outputs found
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
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
Hybrid mapping for static and non-static indoor environments
Mención Internacional en el título de doctorIndoor environments populated by humans, such as houses, offices or universities,
involve a great complexity due to the diversity of geometries and situations that they
may present. Apart from the size of the environment, they can contain multiple rooms
distributed into floors and corridors, repetitive structures and loops, and they can
get as complicated as one can imagine. In addition, the structure and situations that
the environment present may vary over time as objects could be moved, doors can
be frequently opened or closed and places can be used for different purposes. Mobile
robots need to solve these challenging situations in order to successfully operate in
the environment. The main tools that a mobile robot has for dealing with these
situations relate to navigation and perception and comprise mapping, localization,
path planning and map adaptation. In this thesis, we try to address some of the open
problems in robot navigation in non-static indoor environments. We focus on house-like
environments as the work is framed into the HEROITEA research project that aims
attention at helping elderly people with their everyday-life activities at their homes.
This thesis contributes to HEROITEA with a complete robotic mapping system and
map adaptation that grants safe navigation and understanding of the environment.
Moreover, we provide localization and path planning strategies within the resulting
map to further operate in the environment.
The first problem tackled in this thesis is robot mapping in static indoor environments.
We propose a hybrid mapping method that structures the information gathered
from the environment into several maps. The hybrid map contains diverse knowledge of
the environment such as its structure, the navigable and blocked paths, and semantic
knowledge, such as the objects or scenes in the environment. All this information is
separated into different components of the hybrid map that are interconnected so the
system can, at any time, benefit from the information contained in every component.
In addition to the conceptual conception of the hybrid map, we have also developed
building procedures and an exploration algorithm to autonomous build the hybrid
map.
However, indoor environments populated by humans are far from being static as
the environment may change over time. For this reason, the second problem tackled in
this thesis is the adaptation of the map to non-static environments. We propose an
object-based probabilistic map adaptation that calculates the likelihood of moving or
remaining in its place for the different objects in the environment.
Finally, a map is just a description of the environment whose importance is mostly
related to how the map is used. In addition, map representations are more valuable
as long as they offer a wider range of applications. Therefore, the third problem
that we approach in this thesis is exploiting the intrinsic characteristics of the hybrid
map in order to enhance the performance of localization and path planning methods.
The particular objectives of these approaches are precision for robot localization and
efficiency for path planning in terms of execution time and traveled distance.
We evaluate our proposed methods in a diversity of simulated and real-world indoor
environments. In this extensive evaluation, we show that hybrid maps can be efficiently
built and maintained over time and they open up for new possibilities for localization
and path planning. In this thesis, we show an increase in localization precision and
robustness and an improvement in path planning performance.
In sum, this thesis makes several contributions in the context of robot navigation
in indoor environments, and especially in hybrid mapping. Hybrid maps offer higher
efficiency during map building and other applications such as localization and path
planning. In addition, we highlight the necessity of dealing with the dynamics of
indoor environments and the benefits of combining topological, semantic and metric
information to the autonomy of a mobile robot.Los entornos de interiores habitados por personas, como casas, oficinas o universidades,
entrañan una gran complejidad por la diversidad de geometrías y situaciones que pueden
ocurrir. Aparte de las diferencias en tamaño, estos entornos pueden contener muchas
habitaciones organizadas en diferentes plantas o pasillos, pueden presentar estructuras
repetitivas o bucles de tal forma que los entornos pueden llegar a ser tan complejos como
uno se pueda imaginar. Además, la estructura y el estado del entorno pueden variar
con el tiempo, ya que los objetos pueden moverse, las puertas pueden estar cerradas o
abiertas y diferentes espacios pueden ser usados para diferentes propósitos. Los robots
móviles necesitan resolver estas situaciones difíciles para poder funcionar de una forma
satisfactoria. Las principales herramientas que tiene un robot móvil para manejar
estas situaciones están relacionadas con la navegación y la percepción y comprenden el
mapeado, la localización, la planificación de trayectorias y la adaptación del mapa. En
esta tesis, abordamos algunos de los problemas sin resolver de la navegación de robots
móviles en entornos de interiores no estáticos. Nos centramos en entornos tipo casa ya
que este trabajo se enmarca en el proyecto de investigación HEROITEA que se enfoca
en ayudar a personas ancianas en tareas cotidianas del hogar. Esta tesis contribuye al
proyecto HEROITEA con un sistema completo de mapeado y adaptación del mapa
que asegura una navegación segura y la comprensión del entorno. Además, aportamos
métodos de localización y planificación de trayectorias usando el mapa construido para
realizar nuevas tareas en el entorno.
El primer problema que se aborda en esta tesis es el mapeado de entornos de
interiores estáticos por parte de un robot. Proponemos un método de mapeado híbrido
que estructura la información capturada en varios mapas. El mapa híbrido contiene
información sobre la estructura del entorno, las trayectorias libres y bloqueadas y
también incluye información semántica, como los objetos y escenas en el entorno. Toda
esta información está separada en diferentes componentes del mapa híbrido que están
interconectados de tal forma que el sistema puede beneficiarse en cualquier momento
de la información contenida en cada componente. Además de la definición conceptual del mapa híbrido, hemos desarrollado unos procedimientos para construir el mapa y un
algoritmo de exploración que permite que esta construcción se realice autónomamente.
Sin embargo, los entornos de interiores habitados por personas están lejos de ser
estáticos ya que pueden cambiar a lo largo del tiempo. Por esta razón, el segundo
problema que intentamos solucionar en esta tesis es la adaptación del mapa para
entornos no estáticos. Proponemos un método probabilístico de adaptación del mapa
basado en objetos que calcula la probabilidad de que cada objeto en el entorno haya
sido movido o permanezca en su posición anterior.
Para terminar, un mapa es simplemente una descripción del entorno cuya importancia
está principalmente relacionada con su uso. Por ello, los mapas más valiosos
serán los que ofrezcan un rango mayor de aplicaciones. Para abordar este asunto, el
tercer problema que intentamos solucionar es explotar las características intrínsecas del
mapa híbrido para mejorar el desempeño de métodos de localización y de planificación
de trayectorias usando el mapa híbrido. El objetivo principal de estos métodos es
aumentar la precisión en la localización del robot y la eficiencia en la planificación de
trayectorias en relación al tiempo de ejecución y la distancia recorrida.
Hemos evaluado los métodos propuestos en una variedad de entornos de interiores
simulados y reales. En esta extensa evaluación, mostramos que los mapas híbridos
pueden construirse y mantenerse en el tiempo de forma eficiente y que dan lugar a
nuevas posibilidades en cuanto a localización y planificación de trayectorias. En esta
tesis, mostramos un aumento en la precisión y robustez en la localización y una mejora
en el desempeño de la planificación de trayectorias.
En resumen, esta tesis lleva a cabo diversas contribuciones en el ámbito de la
navegación de robots móviles en entornos de interiores, y especialmente en mapeado
híbrido. Los mapas híbridos ofrecen más eficiencia durante la construcción del mapa
y en otras tareas como la localización y la planificación de trayectorias. Además,
resaltamos la necesidad de tratar los cambios en entornos de interiores y los beneficios
de combinar información topológica, semántica y métrica para la autonomía del robot.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Javier González Jiménez.- Vocal: Nancy Marie Amat
Learning Models for Following Natural Language Directions in Unknown Environments
Natural language offers an intuitive and flexible means for humans to
communicate with the robots that we will increasingly work alongside in our
homes and workplaces. Recent advancements have given rise to robots that are
able to interpret natural language manipulation and navigation commands, but
these methods require a prior map of the robot's environment. In this paper, we
propose a novel learning framework that enables robots to successfully follow
natural language route directions without any previous knowledge of the
environment. The algorithm utilizes spatial and semantic information that the
human conveys through the command to learn a distribution over the metric and
semantic properties of spatially extended environments. Our method uses this
distribution in place of the latent world model and interprets the natural
language instruction as a distribution over the intended behavior. A novel
belief space planner reasons directly over the map and behavior distributions
to solve for a policy using imitation learning. We evaluate our framework on a
voice-commandable wheelchair. The results demonstrate that by learning and
performing inference over a latent environment model, the algorithm is able to
successfully follow natural language route directions within novel, extended
environments.Comment: ICRA 201
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Visual robot navigation within large-scale, semi-structured environments
deals with various challenges such as computation intensive path planning
algorithms or insufficient knowledge about traversable spaces. Moreover, many
state-of-the-art navigation approaches only operate locally instead of gaining
a more conceptual understanding of the planning objective. This limits the
complexity of tasks a robot can accomplish and makes it harder to deal with
uncertainties that are present in the context of real-time robotics
applications. In this work, we present Topomap, a framework which simplifies
the navigation task by providing a map to the robot which is tailored for path
planning use. This novel approach transforms a sparse feature-based map from a
visual Simultaneous Localization And Mapping (SLAM) system into a
three-dimensional topological map. This is done in two steps. First, we extract
occupancy information directly from the noisy sparse point cloud. Then, we
create a set of convex free-space clusters, which are the vertices of the
topological map. We show that this representation improves the efficiency of
global planning, and we provide a complete derivation of our algorithm.
Planning experiments on real world datasets demonstrate that we achieve similar
performance as RRT* with significantly lower computation times and storage
requirements. Finally, we test our algorithm on a mobile robotic platform to
prove its advantages.Comment: 8 page
Toward an object-based semantic memory for long-term operation of mobile service robots
Throughout a lifetime of operation, a mobile service robot needs to acquire, store and update its knowledge of a working environment. This includes the ability to identify and track objects in different places, as well as using this information for interaction with humans. This paper introduces a long-term updating mechanism, inspired by the modal model of human memory, to enable a mobile robot to maintain its knowledge of a changing environment. The memory model is integrated with a hybrid map that represents the global topology and local geometry of the environment, as well as the respective 3D location of objects. We aim to enable the robot to use this knowledge to help humans by suggesting the most likely locations of specific objects in its map. An experiment using omni-directional vision demonstrates the ability to track the movements of several objects in a dynamic environment over an extended period of time
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
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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