2,382 research outputs found
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
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 EconomÃa y Competitividad. The research leading to this work was also supported project "Robots sociales para estimulacón fÃsica, 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)
Intelligent Robotic Perception Systems
Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics
Autonomous navigation for guide following in crowded indoor environments
The requirements for assisted living are rapidly changing as the number of elderly
patients over the age of 60 continues to increase. This rise places a high level of stress on
nurse practitioners who must care for more patients than they are capable. As this trend is
expected to continue, new technology will be required to help care for patients. Mobile
robots present an opportunity to help alleviate the stress on nurse practitioners by
monitoring and performing remedial tasks for elderly patients. In order to produce
mobile robots with the ability to perform these tasks, however, many challenges must be
overcome.
The hospital environment requires a high level of safety to prevent patient injury. Any
facility that uses mobile robots, therefore, must be able to ensure that no harm will come
to patients whilst in a care environment. This requires the robot to build a high level of
understanding about the environment and the people with close proximity to the robot.
Hitherto, most mobile robots have used vision-based sensors or 2D laser range finders.
3D time-of-flight sensors have recently been introduced and provide dense 3D point
clouds of the environment at real-time frame rates. This provides mobile robots with
previously unavailable dense information in real-time. I investigate the use of time-of-flight
cameras for mobile robot navigation in crowded environments in this thesis. A
unified framework to allow the robot to follow a guide through an indoor environment
safely and efficiently is presented. Each component of the framework is analyzed in
detail, with real-world scenarios illustrating its practical use.
Time-of-flight cameras are relatively new sensors and, therefore, have inherent problems
that must be overcome to receive consistent and accurate data. I propose a novel and
practical probabilistic framework to overcome many of the inherent problems in this
thesis. The framework fuses multiple depth maps with color information forming a
reliable and consistent view of the world. In order for the robot to interact with the
environment, contextual information is required. To this end, I propose a region-growing
segmentation algorithm to group points based on surface characteristics, surface normal
and surface curvature. The segmentation process creates a distinct set of surfaces,
however, only a limited amount of contextual information is available to allow for
interaction. Therefore, a novel classifier is proposed using spherical harmonics to
differentiate people from all other objects.
The added ability to identify people allows the robot to find potential candidates to
follow. However, for safe navigation, the robot must continuously track all visible
objects to obtain positional and velocity information. A multi-object tracking system is
investigated to track visible objects reliably using multiple cues, shape and color. The
tracking system allows the robot to react to the dynamic nature of people by building an
estimate of the motion flow. This flow provides the robot with the necessary information
to determine where and at what speeds it is safe to drive. In addition, a novel search
strategy is proposed to allow the robot to recover a guide who has left the field-of-view.
To achieve this, a search map is constructed with areas of the environment ranked
according to how likely they are to reveal the guide’s true location. Then, the robot can
approach the most likely search area to recover the guide. Finally, all components
presented are joined to follow a guide through an indoor environment. The results
achieved demonstrate the efficacy of the proposed components
Scene understanding for autonomous robots operating in indoor environments
Mención Internacional en el tÃtulo de doctorThe idea of having robots among us is not new. Great efforts are continually made to
replicate human intelligence, with the vision of having robots performing different activities,
including hazardous, repetitive, and tedious tasks. Research has demonstrated that robots are
good at many tasks that are hard for us, mainly in terms of precision, efficiency, and speed.
However, there are some tasks that humans do without much effort that are challenging for
robots. Especially robots in domestic environments are far from satisfactorily fulfilling some
tasks, mainly because these environments are unstructured, cluttered, and with a variety of
environmental conditions to control.
This thesis addresses the problem of scene understanding in the context of autonomous
robots operating in everyday human environments. Furthermore, this thesis is developed
under the HEROITEA research project that aims to develop a robot system to help
elderly people in domestic environments as an assistant. Our main objective is to develop
different methods that allow robots to acquire more information from the environment to
progressively build knowledge that allows them to improve the performance on high-level
robotic tasks. In this way, scene understanding is a broad research topic, and it is considered
a complex task due to the multiple sub-tasks that are involved. In that context, in this thesis,
we focus on three sub-tasks: object detection, scene recognition, and semantic segmentation
of the environment.
Firstly, we implement methods to recognize objects considering real indoor environments.
We applied machine learning techniques incorporating uncertainties and more modern
techniques based on deep learning. Besides, apart from detecting objects, it is essential to
comprehend the scene where they can occur. For this reason, we propose an approach
for scene recognition that considers the influence of the detected objects in the prediction
process. We demonstrate that the exiting objects and their relationships can improve the
inference about the scene class. We also consider that a scene recognition model can
benefit from the advantages of other models. We propose a multi-classifier model for scene
recognition based on weighted voting schemes. The experiments carried out in real-world
indoor environments demonstrate that the adequate combination of independent classifiers
allows obtaining a more robust and precise model for scene recognition.
Moreover, to increase the understanding of a robot about its surroundings, we propose
a new division of the environment based on regions to build a useful representation of
the environment. Object and scene information is integrated into a probabilistic fashion
generating a semantic map of the environment containing meaningful regions within each
room. The proposed system has been assessed on simulated and real-world domestic
scenarios, demonstrating its ability to generate consistent environment representations.
Lastly, full knowledge of the environment can enhance more complex robotic tasks; that is
why in this thesis, we try to study how a complete knowledge of the environment influences
the robot’s performance in high-level tasks. To do so, we select an essential task, which
is searching for objects. This mundane task can be considered a precondition to perform
many complex robotic tasks such as fetching and carrying, manipulation, user requirements,
among others. The execution of these activities by service robots needs full knowledge of
the environment to perform each task efficiently. In this thesis, we propose two searching
strategies that consider prior information, semantic representation of the environment, and
the relationships between known objects and the type of scene. All our developments are
evaluated in simulated and real-world environments, integrated with other systems, and
operating in real platforms, demonstrating their feasibility to implement in real scenarios, and
in some cases outperforming other approaches. We also demonstrate how our representation
of the environment can boost the performance of more complex robotic tasks compared to
more standard environmental representations.La idea de tener robots entre nosotros no es nueva. Continuamente se realizan grandes
esfuerzos para replicar la inteligencia humana, con la visión de tener robots que realicen
diferentes actividades, incluidas tareas peligrosas, repetitivas y tediosas. La investigación ha
demostrado que los robots son buenos en muchas tareas que resultan difÃciles para nosotros,
principalmente en términos de precisión, eficiencia y velocidad. Sin embargo, existen tareas
que los humanos realizamos sin mucho esfuerzo y que son un desafÃo para los robots.
Especialmente, los robots en entornos domésticos están lejos de cumplir satisfactoriamente
algunas tareas, principalmente porque estos entornos no son estructurados, pueden estar
desordenados y cuentan con una gran variedad de condiciones ambientales que controlar.
Esta tesis aborda el problema de la comprensión de la escena en el contexto de robots
autónomos que operan en entornos humanos cotidianos. Asimismo, esta tesis se desarrolla
en el marco del proyecto de investigación HEROITEA que tiene como objetivo desarrollar
un sistema robótico que funcione como asistente para ayudar a personas mayores en entornos
domésticos. Nuestro principal objetivo es desarrollar diferentes métodos que permitan a
los robots adquirir más información del entorno a fin de construir progresivamente un
conocimiento que les permita mejorar su desempeño en tareas robóticas más complejas.
En este sentido, la comprensión de escenas es un tema de investigación amplio, y se
considera una tarea compleja debido a las múltiples subtareas involucradas. En esta tesis
nos enfocamos especÃficamente en tres subtareas: detección de objetos, reconocimiento de
escenas y etiquetado semántico del entorno.
Por un lado, implementamos métodos para el reconocimiento de objectos considerando
entornos interiores reales. Aplicamos técnicas de aprendizaje automático incorporando
incertidumbres y técnicas más modernas basadas en aprendizaje profundo. Además, aparte
de detectar objetos, es fundamental comprender la escena donde estos se encuentran. Por esta
razón, proponemos un modelo para el reconocimiento de escenas que considera la influencia
de los objetos detectados en el proceso de predicción. Demostramos que los objetos existentes
y sus relaciones pueden mejorar el proceso de inferencia de la categorÃa de la escena. También
consideramos que un modelo de reconocimiento de escenas puede beneficiarse de las ventajas
de otros modelos. Por ello, proponemos un multiclasificador para el reconocimiento de escenas basado en esquemas de votación ponderados. Los experimentos llevados a cabo
en entornos interiores reales demuestran que la combinación adecuada de clasificadores
independientes permite obtener un modelo más robusto y preciso para el reconocimiento
de escenas.
Adicionalmente, para aumentar la comprensión de un robot acerca de su entorno,
proponemos una nueva división del entorno basada en regiones a fin de construir una
representación útil del entorno. La información de objetos y de la escena se integra de forma
probabilÃstica generando un mapa semántico que contiene regiones significativas dentro de
cada habitación. El sistema propuesto ha sido evaluado en entornos domésticos simulados y
reales, demostrando su capacidad para generar representaciones consistentes del entorno.
Por otro lado, el conocimiento integral del entorno puede mejorar tareas robóticas más
complejas; es por ello que en esta tesis analizamos cómo el conocimiento completo del
entorno influye en el desempeño del robot en tareas de alto nivel. Para ello, seleccionamos una
tarea fundamental, que es la búsqueda de objetos. Esta tarea mundana puede considerarse
una condición previa para realizar diversas tareas robóticas complejas, como transportar
objetos, tareas de manipulación, atender requerimientos del usuario, entre otras. La
ejecución de estas actividades por parte de robots de servicio requiere un conocimiento
profundo del entorno para realizar cada tarea de manera eficiente. En esta tesis proponemos
dos estrategias de búsqueda de objetos que consideran información previa, la representación
semántica del entorno, las relaciones entre los objetos conocidos y el tipo de escena. Todos
nuestros desarrollos son evaluados en entornos simulados y reales, integrados con otros
sistemas y operando en plataformas reales, demostrando su viabilidad de ser implementados
en escenarios reales y, en algunos casos, superando a otros enfoques. También demostramos
cómo nuestra representación del entorno puede mejorar el desempeño de tareas robóticas
más complejas en comparación con representaciones del entorno más tradicionales.Programa de Doctorado en IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Fernando MatÃa Espada.- Vocal: Klaus Strob
Mobile Robot Navigation in Indoor Environments: Geometric, Topological, and Semantic Navigation
The objective of the chapter is to show current trends in robot navigation systems related to indoor environments. Navigation systems depend on the level of abstraction of the environment representation. The three main techniques for representing the environment will be described: geometric, topological, and semantic. The geometric representation of the environment is closer to the sensor and actuator world and it is the best one to perform local navigation. Topological representation of the environment uses graphs to model the environment and it is used in large navigation tasks. The semantic representation is the most abstract representation model and adds concepts such as utilities or meanings of the environment elements in the map representation. In addition, regardless of the representation used for navigation, perception plays a significant role in terms of understanding and moving through the environment
Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges
3D Reconstruction of Indoor Corridor Models Using Single Imagery and Video Sequences
In recent years, 3D indoor modeling has gained more attention due to its role in decision-making process of maintaining the status and managing the security of building indoor spaces. In this thesis, the problem of continuous indoor corridor space modeling has been tackled through two approaches. The first approach develops a modeling method based on middle-level perceptual organization. The second approach develops a visual Simultaneous Localisation and Mapping (SLAM) system with model-based loop closure.
In the first approach, the image space was searched for a corridor layout that can be converted into a geometrically accurate 3D model. Manhattan rule assumption was adopted, and indoor corridor layout hypotheses were generated through a random rule-based intersection of image physical line segments and virtual rays of orthogonal vanishing points. Volumetric reasoning, correspondences to physical edges, orientation map and geometric context of an image are all considered for scoring layout hypotheses. This approach provides physically plausible solutions while facing objects or occlusions in a corridor scene.
In the second approach, Layout SLAM is introduced. Layout SLAM performs camera localization while maps layout corners and normal point features in 3D space. Here, a new feature matching cost function was proposed considering both local and global context information. In addition, a rotation compensation variable makes Layout SLAM robust against cameras orientation errors accumulations. Moreover, layout model matching of keyframes insures accurate loop closures that prevent miss-association of newly visited landmarks to previously visited scene parts.
The comparison of generated single image-based 3D models to ground truth models showed that average ratio differences in widths, heights and lengths were 1.8%, 3.7% and 19.2% respectively. Moreover, Layout SLAM performed with the maximum absolute trajectory error of 2.4m in position and 8.2 degree in orientation for approximately 318m path on RAWSEEDS data set. Loop closing was strongly performed for Layout SLAM and provided 3D indoor corridor layouts with less than 1.05m displacement errors in length and less than 20cm in width and height for approximately 315m path on York University data set. The proposed methods can successfully generate 3D indoor corridor models compared to their major counterpart
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