656 research outputs found
Incremental topological mapping using omnidirectional vision
This paper presents an algorithm that builds topological maps, using omnidirectional vision as the only sensor modality. Local features are extracted from images obtained in sequence, and are used both to cluster the images into nodes and to detect links between the nodes. The algorithm is incremental, reducing the computational requirements of the corresponding batch algorithm. Experimental results in a complex, indoor environment show that the algorithm produces topologically correct maps, closing loops without suffering from perceptual aliasing or false links. Robustness to lighting variations was further demonstrated by building correct maps from combined multiple datasets collected over a period of 2 month
Incremental spectral clustering and its application to topological mapping
This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the
spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments
show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the
algorithm
An adaptive appearance-based map for long-term topological localization of mobile robots
This work considers a mobile service robot which uses an appearance-based representation of its workplace as a map, where the current view and the map are used to estimate the current position in the environment. Due to the nature of real-world environments such as houses and offices, where the appearance keeps changing, the internal representation may become out of date after some time. To solve this problem the robot needs to be able to adapt its internal representation continually to the changes in the environment. This paper presents a method for creating an adaptive map for long-term appearance-based localization of a mobile robot using long-term and short-term memory concepts, with omni-directional vision as the external sensor
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 previous work we introduced a method to update the reference views in a topological map so that a mobile robot could continue to localize itself in a changing environment using omni-directional vision. In this work we extend this longterm updating mechanism to incorporate a spherical metric representation of the observed visual features for each node in the topological map. Using multi-view geometry we are then able to estimate the heading of the robot, in order to enable navigation between the nodes of the map, and to simultaneously adapt the spherical view representation in response to environmental changes. The results demonstrate the persistent performance of the proposed system in a long-term experiment
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
Map Building and Monte Carlo Localization Using Global Appearance of Omnidirectional Images
In this paper we deal with the problem of map building and localization of a mobile robot in an environment using the information provided by an omnidirectional vision sensor that is mounted on the robot. Our main objective consists of studying the feasibility of the techniques based in the global appearance of a set of omnidirectional images captured by this vision sensor to solve this problem. First, we study how to describe globally the visual information so that it represents correctly locations and the geometrical relationships between these locations. Then, we integrate this information using an approach based on a spring-mass-damper model, to create a topological map of the environment. Once the map is built, we propose the use of a Monte Carlo localization approach to estimate the most probable pose of the vision system and its trajectory within the map. We perform a comparison in terms of computational cost and error in localization. The experimental results we present have been obtained with real indoor omnidirectional images
Incremental vision-based topological SLAM
Published versio
Augmented indoor hybrid maps using catadioptric vision
En este Trabajo de Fin de Máster se presenta un nuevo método para crear mapas semánticos a partir de secuencias de imágenes omnidireccionales. El objetivo es diseñar el nivel superior de un mapa jerárquico: mapa semántico o mapa topológico aumentado, aprovechando y adaptando este tipo de cámaras. La segmentación de la secuencia de imágenes se realiza distinguiendo entre Lugares y Transiciones, poniendo especial énfasis en la detección de estas Transiciones ya que aportan una información muy útil e importante al mapa. Dentro de los Lugares se hace una clasificación más detallada entre pasillos y habitaciones de distintos tipos. Y dentro de las Transiciones distinguiremos entre puertas, jambas, escaleras y ascensores, que son los principales tipos de Transiciones que aparecen en escenarios de interior. Para la segmentación del espacio en estos tipos de áreas se han utilizado solo descriptores de imagen globales, en concreto Gist. La gran ventaja de usar este tipo de descriptores es la mayor eficiencia y compacidad frente al uso de descriptores locales. Además para mantener la consistencia espacio-temporal de la secuencia de imágenes, se hace uso de un modelo probabilístico: Modelo Oculto de Markov (HMM). A pesar de la simplicidad del método, los resultados muestran cómo es capaz de realizar una segmentación de la secuencia de imágenes en clusters con significado para las personas. Todos los experimentos se han llevado a cabo utilizando nuestro nuevo data set de imágenes omnidireccionales, capturado con una cámara montada en un casco, por lo que la secuencia sigue el movimiento de una persona durante su desplazamiento dentro de un edificio. El data set se encuentra público en Internet para que pueda ser utilizado en otras investigaciones
Creation and maintenance of visual incremental maps and hierarchical localization.
Over the last few years, the presence of the mobile robotics has considerably
increased in a wide variety of environments. It is common to find robots that carry
out repetitive and specific applications and also, they can be used for working at
dangerous environments and to perform precise tasks. These robots can be
found in a variety of social environments, such as industry, household,
educational and health scenarios. For that reason, they need a specific and
continuous research and improvement work. Specifically, autonomous mobile
robots require a very precise technology to perform tasks without human
assistance.
To perform tasks autonomously, the robots must be able to navigate in an
unknown environment. For that reason, the autonomous mobile robots must be
able to address the mapping and localization tasks: they must create a model of
the environment and estimate their position and orientation.
This PhD thesis proposes and analyses different methods to carry out the map
creation and the localization tasks in indoor environments. To address these
tasks only visual information is used, specifically, omnidirectional images, with a
360º field of view. Throughout the chapters of this document solutions for
autonomous navigation tasks are proposed, they are solved using
transformations in the images captured by a vision system mounted on the robot.
Firstly, the thesis focuses on the study of the global appearance descriptors in
the localization task. The global appearance descriptors are algorithms that
transform an image globally, into a unique vector. In these works, a deep
comparative study is performed. In the experiments different global appearance
descriptors are used along with omnidirectional images and the results are
compared. The main goal is to obtain an optimized algorithm to estimate the robot
position and orientation in real indoor environments. The experiments take place
with real conditions, so some visual changes in the scenes can occur, such as
camera defects, furniture or people movements and changes in the lighting
conditions. The computational cost is also studied; the idea is that the robot has
to localize the robot in an accurate mode, but also, it has to be fast enought.
Additionally, a second application, whose goal is to carry out an incremental
mapping in indoor environments, is presented. This application uses the best
global appearance descriptors used in the localization task, but this time they are
constructed with the purpose of solving the mapping problem using an
incremental clustering technique. The application clusters a batch of images that
are visually similar; every group of images or cluster is expected to identify a zone
of the environment. The shape and size of the cluster can vary while the robot is
visiting the different rooms. Nowadays. different algorithms can be used to obtain
the clusters, but all these solutions usually work properly when they work ‘offline’,
starting from the whole set of data to cluster. The main idea of this study is
to obtain the map incrementally while the robot explores the new environment.
Carrying out the mapping incrementally while the robot is still visiting the area is very interesting since having the map separated into nodes with relationships of
similitude between them can be used subsequently for the hierarchical
localization tasks, and also, to recognize environments already visited in the
model.
Finally, this PhD thesis includes an analysis of deep learning techniques for
localization tasks. Particularly, siamese networks have been studied. Siamese
networks are based on classic convolutional networks, but they permit evaluating
two images simultaneously. These networks output a similarity value between the
input images, and that information can be used for the localization tasks.
Throughout this work the technique is presented, the possible architectures are
analysed and the results after the experiments are shown and compared. Using
the siamese networks, the localization in real operation conditions and
environments is solved, focusing on improving the performance against
illumination changes on the scene. During the experiments the room retrieval
problem, the hierarchical localization and the absolute localization have been
solved.Durante los últimos años, la presencia de la robótica móvil ha aumentado
substancialmente en una gran variedad de entornos y escenarios. Es habitual
encontrar el uso de robots para llevar a cabo aplicaciones repetitivas y
específicas, así como tareas en entornos peligrosos o con resultados que deben
ser muy precisos. Dichos robots se pueden encontrar tanto en ámbitos
industriales como en familiares, educativos y de salud; por ello, requieren un
trabajo específico y continuo de investigación y mejora. En concreto, los robots
móviles autónomos requieren de una tecnología precisa para desarrollar tareas
sin ayuda del ser humano.
Para realizar tareas de manera autónoma, los robots deben ser capaces de
navegar por un entorno ‘a priori’ desconocido. Por tanto, los robots móviles
autónomos deben ser capaces de realizar la tarea de creación de mapas,
creando un modelo del entorno y la tarea de localización, esto es estimar su
posición y orientación.
La presente tesis plantea un diseño y análisis de diferentes métodos para realizar
las tareas de creación de mapas y localización en entornos de interior. Para estas
tareas se emplea únicamente información visual, en concreto, imágenes
omnidireccionales, con un campo de visión de 360º. En los capítulos de este
trabajo se plantean soluciones a las tareas de navegación autónoma del robot
mediante transformaciones en las imágenes que este es capaz de captar.
En cuanto a los trabajos realizados, en primer lugar, se presenta un estudio de
descriptores de apariencia global en tareas de localización. Los descriptores de
apariencia global son transformaciones capaces de obtener un único vector que
describa globalmente una imagen. En este trabajo se realiza un estudio
exhaustivo de diferentes métodos de apariencia global adaptando su uso a
imágenes omnidireccionales. Se trata de obtener un algoritmo optimizado para
estimar la posición y orientación del robot en entornos reales de oficina, donde
puede surgir cambios visuales en el entorno como movimientos de cámara, de
mobiliario o de iluminación en la escena. También se evalúa el tiempo empleado
para realizar esta estimación, ya que el trabajo de un robot debe ser preciso,
pero también factible en cuanto a tiempos de computación.
Además, se presenta una segunda aplicación donde el estudio se centra en la
creación de mapas de entornos de interior de manera incremental. Esta
aplicación hace uso de los descriptores de apariencia global estudiados para la
tarea de localización, pero en este caso se utilizan para la construcción de mapas
utilizando la técnica de ‘clustering’ incremental. En esta aplicación, conjuntos de
imágenes visualmente similares se agrupan en un único grupo. La forma y
cantidad de grupos es variable conforme el robot avanza en el entorno.
Actualmente, existen diferentes algoritmos para obtener la separación de un
entorno en nodos, pero las soluciones efectivas se realizan de manera ‘off-line’,
es decir, a posteriori una vez se tienen todas las imágenes captadas. El trabajo
presentado permite realizar esta tarea de manera incremental mientras el robot explora el nuevo entorno. Realizar esta tarea mientras se visita el resto del
entorno puede ser muy interesante ya que tener el mapa separado por nodos
con relaciones de proximidad entre ellos se puede ir utilizando para tareas de
localización jerárquica. Además, es posible reconocer entornos ya visitados o
similares a nodos pasados.
Por último, la tesis también incluye el estudio de técnicas de aprendizaje
profundo (‘deep learning’) para tareas de localización. En concreto, se estudia el
uso de las redes siamesas, una técnica poco explorada en robótica móvil, que
está basada en las clásicas redes convolucionales, pero en la que dos imágenes
son evaluadas al mismo tiempo. Estas redes dan un valor de similitud entre el
par de imágenes de entrada, lo que permite realizar tareas de localización visual.
En este trabajo se expone esta técnica, se presentan las estructuras que pueden
tener estas redes y los resultados tras la experimentación. Se evalúa la tarea de
localización en entornos heterogéneos en los que el principal problema viene
dado por cambios en la iluminación de la escena. Con las redes siamesas se
trata de resolver el problema de estimación de estancia, el problema de
localización jerárquica y el de localización absoluta
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