25 research outputs found
Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders
This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality
Adapting Monte Carlo Localization to Utilize Floor and Wall Texture Data
Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input.
Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations.
The best variations of Texture MCL improved accuracy by 19.1\% and 25.1\% when all particles and the top fifty particles respectively were used to calculate the robot\u27s estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot
Scene understanding for autonomous robots operating in indoor environments
Menci贸n Internacional en el t铆tulo 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鈥檚 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 visi贸n de tener robots que realicen
diferentes actividades, incluidas tareas peligrosas, repetitivas y tediosas. La investigaci贸n ha
demostrado que los robots son buenos en muchas tareas que resultan dif铆ciles para nosotros,
principalmente en t茅rminos de precisi贸n, eficiencia y velocidad. Sin embargo, existen tareas
que los humanos realizamos sin mucho esfuerzo y que son un desaf铆o para los robots.
Especialmente, los robots en entornos dom茅sticos est谩n 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 comprensi贸n de la escena en el contexto de robots
aut贸nomos que operan en entornos humanos cotidianos. Asimismo, esta tesis se desarrolla
en el marco del proyecto de investigaci贸n HEROITEA que tiene como objetivo desarrollar
un sistema rob贸tico que funcione como asistente para ayudar a personas mayores en entornos
dom茅sticos. Nuestro principal objetivo es desarrollar diferentes m茅todos que permitan a
los robots adquirir m谩s informaci贸n del entorno a fin de construir progresivamente un
conocimiento que les permita mejorar su desempe帽o en tareas rob贸ticas m谩s complejas.
En este sentido, la comprensi贸n de escenas es un tema de investigaci贸n amplio, y se
considera una tarea compleja debido a las m煤ltiples subtareas involucradas. En esta tesis
nos enfocamos espec铆ficamente en tres subtareas: detecci贸n de objetos, reconocimiento de
escenas y etiquetado sem谩ntico del entorno.
Por un lado, implementamos m茅todos para el reconocimiento de objectos considerando
entornos interiores reales. Aplicamos t茅cnicas de aprendizaje autom谩tico incorporando
incertidumbres y t茅cnicas m谩s modernas basadas en aprendizaje profundo. Adem谩s, aparte
de detectar objetos, es fundamental comprender la escena donde estos se encuentran. Por esta
raz贸n, proponemos un modelo para el reconocimiento de escenas que considera la influencia
de los objetos detectados en el proceso de predicci贸n. Demostramos que los objetos existentes
y sus relaciones pueden mejorar el proceso de inferencia de la categor铆a de la escena. Tambi茅n
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 votaci贸n ponderados. Los experimentos llevados a cabo
en entornos interiores reales demuestran que la combinaci贸n adecuada de clasificadores
independientes permite obtener un modelo m谩s robusto y preciso para el reconocimiento
de escenas.
Adicionalmente, para aumentar la comprensi贸n de un robot acerca de su entorno,
proponemos una nueva divisi贸n del entorno basada en regiones a fin de construir una
representaci贸n 煤til del entorno. La informaci贸n de objetos y de la escena se integra de forma
probabil铆stica generando un mapa sem谩ntico que contiene regiones significativas dentro de
cada habitaci贸n. El sistema propuesto ha sido evaluado en entornos dom茅sticos simulados y
reales, demostrando su capacidad para generar representaciones consistentes del entorno.
Por otro lado, el conocimiento integral del entorno puede mejorar tareas rob贸ticas m谩s
complejas; es por ello que en esta tesis analizamos c贸mo el conocimiento completo del
entorno influye en el desempe帽o del robot en tareas de alto nivel. Para ello, seleccionamos una
tarea fundamental, que es la b煤squeda de objetos. Esta tarea mundana puede considerarse
una condici贸n previa para realizar diversas tareas rob贸ticas complejas, como transportar
objetos, tareas de manipulaci贸n, atender requerimientos del usuario, entre otras. La
ejecuci贸n 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 b煤squeda de objetos que consideran informaci贸n previa, la representaci贸n
sem谩ntica 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. Tambi茅n demostramos
c贸mo nuestra representaci贸n del entorno puede mejorar el desempe帽o de tareas rob贸ticas
m谩s complejas en comparaci贸n con representaciones del entorno m谩s tradicionales.Programa de Doctorado en Ingenier铆a El茅ctrica, Electr贸nica y Autom谩tica por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quir贸s.- Secretario: Fernando Mat铆a Espada.- Vocal: Klaus Strob
Object Tracking
Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application
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
Deep Learning-Based Robotic Perception for Adaptive Facility Disinfection
Hospitals, schools, airports, and other environments built for mass gatherings can become hot spots for microbial pathogen colonization, transmission, and exposure, greatly accelerating the spread of infectious diseases across communities, cities, nations, and the world. Outbreaks of infectious diseases impose huge burdens on our society. Mitigating the spread of infectious pathogens within mass-gathering facilities requires routine cleaning and disinfection, which are primarily performed by cleaning staff under current practice. However, manual disinfection is limited in terms of both effectiveness and efficiency, as it is labor-intensive, time-consuming, and health-undermining. While existing studies have developed a variety of robotic systems for disinfecting contaminated surfaces, those systems are not adequate for intelligent, precise, and environmentally adaptive disinfection. They are also difficult to deploy in mass-gathering infrastructure facilities, given the high volume of occupants. Therefore, there is a critical need to develop an adaptive robot system capable of complete and efficient indoor disinfection.
The overarching goal of this research is to develop an artificial intelligence (AI)-enabled robotic system that adapts to ambient environments and social contexts for precise and efficient disinfection. This would maintain environmental hygiene and health, reduce unnecessary labor costs for cleaning, and mitigate opportunity costs incurred from infections. To these ends, this dissertation first develops a multi-classifier decision fusion method, which integrates scene graph and visual information, in order to recognize patterns in human activity in infrastructure facilities. Next, a deep-learning-based method is proposed for detecting and classifying indoor objects, and a new mechanism is developed to map detected objects in 3D maps. A novel framework is then developed to detect and segment object affordance and to project them into a 3D semantic map for precise disinfection. Subsequently, a novel deep-learning network, which integrates multi-scale features and multi-level features, and an encoder network are developed to recognize the materials of surfaces requiring disinfection. Finally, a novel computational method is developed to link the recognition of object surface information to robot disinfection actions with optimal disinfection parameters