Currently, Clara Gómez is a PhD student at University Carlos III of Madrid and her research area includes navigation, localization and path-planning with mobile robots.
She graduated in Electronic Engineering and Automation by University Carlos III of Madrid and she received her MSc. degree in Robotics and Automation in 2016 obtaining Extraordinary mention.
To move around the environment, human beings depend on sight more than their other senses, becaus... more To move around the environment, human beings depend on sight more than their other senses, because it provides information about the size, shape, color and position of an object. The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in indoor environments a very important and challenging task. In this work, a vision system to detect objects considering usual human environments, able to work on a real mobile robot, is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Different segmentation techniques have been applied to each kind of object. Similarly, two alternatives to extract features of the objects are explored, based on geometric shape descriptors and bag of words. The experimental results have demonstrated the usefulness of the system for the detection and location of the objects in indoor environments. Furthermore, through the comparison of two proposed methods for extracting features, it has been determined which alternative offers better performance. The final results have been obtained taking into account the proposed problem and that the environment has not been changed, that is to say, the environment has not been altered to perform the tests.
Localization is the process of knowing and updating
continuously a robot position with regards to... more Localization is the process of knowing and updating continuously a robot position with regards to its environment based on sensor information. Localization strategies are required for accurate mobile robot navigation and they should be adapted to the new tendencies and tools. The main purpose of this work is to develop a localization system that allows a mobile robot to know its position at each moment as well as to identify when it has gotten lost. In this paper, localization is applied to a topologically defined environment using Hidden Markov Models (HMMs) as main probability algorithm. HMMs give a stochastic solution applicable to discrete representations such as events associated to sensorial actions. The developed topological localization system requires an a priori environment representation, the acquisition of perceptions related to events, the planning of a path as a sequence of actions and perceptions and the navigation that converts the sequence into real movements. Finally, experiments have been carried out in a simulated environment, their results show the feasibility of the localization system and motivates the future test in real robotic platforms. The results also encourage to integrate topological and metric information in the probability distributions.
—The increasing interest in building autonomous mobile systems makes the detection and recognitio... more —The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in natural environments is a very important and challenging task. In this paper, a vision system to detect objects considering natural environments, able to work on real mobile robot is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Two approaches for implementing the selected classification method are explored, the prediction process one against all and one against one. The experimental results have demonstrated the usefulness of the system for detection and location of objects, and through the comparison of the two proposed approaches for the classification, has been determined which alternative offers better performance considering that the environment has not been changed, guaranteeing the naturalness of the place.
The aim of the work presented in this article is to develop a navigation system that allows a mob... more The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.
A Semantic Labeling of the Environment Based on What People Do, 2017
In this work, a system is developed for semantic labeling of locations based on what people do. T... more In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.
To move around the environment, human beings depend on sight more than their other senses, becaus... more To move around the environment, human beings depend on sight more than their other senses, because it provides information about the size, shape, color and position of an object. The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in indoor environments a very important and challenging task. In this work, a vision system to detect objects considering usual human environments, able to work on a real mobile robot, is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Different segmentation techniques have been applied to each kind of object. Similarly, two alternatives to extract features of the objects are explored, based on geometric shape descriptors and bag of words. The experimental results have demonstrated the usefulness of the system for the detection and location of the objects in indoor environments. Furthermore, through the comparison of two proposed methods for extracting features, it has been determined which alternative offers better performance. The final results have been obtained taking into account the proposed problem and that the environment has not been changed, that is to say, the environment has not been altered to perform the tests.
Localization is the process of knowing and updating
continuously a robot position with regards to... more Localization is the process of knowing and updating continuously a robot position with regards to its environment based on sensor information. Localization strategies are required for accurate mobile robot navigation and they should be adapted to the new tendencies and tools. The main purpose of this work is to develop a localization system that allows a mobile robot to know its position at each moment as well as to identify when it has gotten lost. In this paper, localization is applied to a topologically defined environment using Hidden Markov Models (HMMs) as main probability algorithm. HMMs give a stochastic solution applicable to discrete representations such as events associated to sensorial actions. The developed topological localization system requires an a priori environment representation, the acquisition of perceptions related to events, the planning of a path as a sequence of actions and perceptions and the navigation that converts the sequence into real movements. Finally, experiments have been carried out in a simulated environment, their results show the feasibility of the localization system and motivates the future test in real robotic platforms. The results also encourage to integrate topological and metric information in the probability distributions.
—The increasing interest in building autonomous mobile systems makes the detection and recognitio... more —The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in natural environments is a very important and challenging task. In this paper, a vision system to detect objects considering natural environments, able to work on real mobile robot is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Two approaches for implementing the selected classification method are explored, the prediction process one against all and one against one. The experimental results have demonstrated the usefulness of the system for detection and location of objects, and through the comparison of the two proposed approaches for the classification, has been determined which alternative offers better performance considering that the environment has not been changed, guaranteeing the naturalness of the place.
The aim of the work presented in this article is to develop a navigation system that allows a mob... more The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.
A Semantic Labeling of the Environment Based on What People Do, 2017
In this work, a system is developed for semantic labeling of locations based on what people do. T... more In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.
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Papers by CLARA GOMEZ BLAZQUEZ
continuously a robot position with regards to its environment
based on sensor information. Localization strategies are required
for accurate mobile robot navigation and they should be adapted
to the new tendencies and tools. The main purpose of this work
is to develop a localization system that allows a mobile robot to
know its position at each moment as well as to identify when it
has gotten lost.
In this paper, localization is applied to a topologically defined
environment using Hidden Markov Models (HMMs) as
main probability algorithm. HMMs give a stochastic solution
applicable to discrete representations such as events associated
to sensorial actions. The developed topological localization system
requires an a priori environment representation, the acquisition
of perceptions related to events, the planning of a path as a
sequence of actions and perceptions and the navigation that
converts the sequence into real movements.
Finally, experiments have been carried out in a simulated
environment, their results show the feasibility of the localization
system and motivates the future test in real robotic platforms.
The results also encourage to integrate topological and metric
information in the probability distributions.
autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on
events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing
interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The
system proposed can be considered a perceptive navigation system as the navigation process is based on perception and
recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration
interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The
developed architecture enhances the integration of new elements due to its modularity and the decoupling between
modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the
navigation system proposed and the effectiveness of the sensorial data integration managed as events.
continuously a robot position with regards to its environment
based on sensor information. Localization strategies are required
for accurate mobile robot navigation and they should be adapted
to the new tendencies and tools. The main purpose of this work
is to develop a localization system that allows a mobile robot to
know its position at each moment as well as to identify when it
has gotten lost.
In this paper, localization is applied to a topologically defined
environment using Hidden Markov Models (HMMs) as
main probability algorithm. HMMs give a stochastic solution
applicable to discrete representations such as events associated
to sensorial actions. The developed topological localization system
requires an a priori environment representation, the acquisition
of perceptions related to events, the planning of a path as a
sequence of actions and perceptions and the navigation that
converts the sequence into real movements.
Finally, experiments have been carried out in a simulated
environment, their results show the feasibility of the localization
system and motivates the future test in real robotic platforms.
The results also encourage to integrate topological and metric
information in the probability distributions.
autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on
events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing
interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The
system proposed can be considered a perceptive navigation system as the navigation process is based on perception and
recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration
interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The
developed architecture enhances the integration of new elements due to its modularity and the decoupling between
modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the
navigation system proposed and the effectiveness of the sensorial data integration managed as events.