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

Knowledge Representation for Robots through Human-Robot Interaction

The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP 201

Categorization of indoor places using the Kinect sensor

The categorization of places in indoor environments is an important capability for service robots working and interacting with humans. In this paper we present a method to categorize different areas in indoor environments using a mobile robot equipped with a Kinect camera. Our approach transforms depth and grey scale images taken at each place into histograms of local binary patterns (LBPs) whose dimensionality is further reduced following a uniform criterion. The histograms are then combined into a single feature vector which is categorized using a supervised method. In this work we compare the performance of support vector machines and random forests as supervised classifiers. Finally, we apply our technique to distinguish five different place categories: corridors, laboratories, offices, kitchens, and study rooms. Experimental results show that we can categorize these places with high accuracy using our approach

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

Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living

The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning Topometric Semantic Maps from Occupancy Grids

Today's mobile robots are expected to operate in complex environments they share with humans. To allow intuitive human-robot collaboration, robots require a human-like understanding of their surroundings in terms of semantically classified instances. In this paper, we propose a new approach for deriving such instance-based semantic maps purely from occupancy grids. We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a random-sized map. The extraction is followed by a post-processing chain to further increase the accuracy of our approach, as well as place categorization for the three classes room, door and corridor. All detected and classified entities are described as instances specified in a common coordinate system, while a topological map is derived to capture their spatial links. To train our two neural networks used for detection and map segmentation, we contribute a simulator that automatically creates and annotates the required training data. We further provide insight into which features are learned to detect doorways, and how the simulated training data can be augmented to train networks for the direct application on real-world grid maps. We evaluate our approach on several publicly available real-world data sets. Even though the used networks are solely trained on simulated data, our approach demonstrates high robustness and effectiveness in various real-world indoor environments.Comment: Presented at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Efficient semantic place categorization by a robot through active line-of-sight selection

In this paper, we present an attention mechanism for mobile robots to face the problem of place categorization. Our approach, which is based on active perception, aims to capture images with characteristic or distinctive details of the environment that can be exploited to improve the efficiency (quickness and accuracy) of the place categorization. To do so, at each time moment, our proposal selects the most informative view by controlling the line-of-sight of the robot’s camera through a pan-only unit. We root our proposal on an information maximization scheme, formalized as a next-best-view problem through a Markov Decision Process (MDP) model. The latter exploits the short-time estimated navigation path of the robot to anticipate the next robot’s movements and make consistent decisions. We demonstrate over two datasets, with simulated and real data, that our proposal generalizes well for the two main paradigms of place categorization (object-based and image-based), outperforming typical camera-configurations (fixed and continuously-rotating) and a pure-exploratory approach, both in quickness and accuracy.This work was supported by the research projects WISER (DPI2017-84827-R) and ARPEGGIO (PID2020-117057), as well as by the Spanish grant program FPU19/00704. Funding for open access charge: Universidad de Málaga / CBUA

Probabilistic techniques in semantic mapping for mobile robotics

Los mapas semánticos son representaciones del mundo que permiten a un robot entender no sólo los aspectos espaciales de su lugar de trabajo, sino también el significado de sus elementos (objetos, habitaciones, etc.) y como los humanos interactúan con ellos (e.g. funcionalidades, eventos y relaciones). Para conseguirlo, un mapa semántico añade a las representaciones puramente espaciales, tales como mapas geométricos o topológicos, meta-información sobre los tipos de elementos y relaciones que pueden encontrarse en el entorno de trabajo. Esta meta-información, denominada conocimiento semántico o de sentido común, se codifica típicamente en Bases de Conocimiento. Un ejemplo de este tipo de información podría ser: "los frigoríficos son objetos grandes, con forma rectangular, colocados normalmente en las cocinas, y que pueden contener comida perecedera y medicación". Codificar y manejar este conocimiento semántico permite al robot razonar acerca de la información obtenida de un cierto lugar de trabajo, así como inferir nueva información con el fin de ejecutar eficientemente tareas de alto nivel como "¡hola robot! llévale la medicación a la abuela, por favor". La presente tesis propone la utilización de técnicas probabilísticas para construir y mantener mapas semánticos, lo cual presenta tres ventajas principales en comparación con los enfoques tradicionales: i) permite manejar incertidumbre (proveniente de los sensores imprecisos del robot y de los modelos empleados), ii) provee representaciones del entorno coherentes por medio del aprovechamiento de las relaciones contextuales entre los elementos observados (e.g. los frigoríficos usualmente se encuentran en las cocinas) desde un punto de vista holístico, y iii) produce valores de certidumbre que reflejan el grado de exactitud de la comprensión del robot acerca de su entorno. Específicamente, las contribuciones presentadas pueden agruparse en dos temas principales. El primer conjunto de contribuciones se basa en el problema del reconocimiento de objetos y/o habitaciones, ya que los sistemas de mapeo semántico deben contar con algoritmos de reconocimiento fiables para la construcción de representaciones válidas. Para ello se ha explorado la utilización de Modelos Gráficos Probabilísticos (Probabilistic Graphical Models o PGMs en inglés) con el fin de aprovechar las relaciones de contexto entre objetos y/o habitaciones a la vez que se maneja la incertidumbre inherente al problema de reconocimiento, y el empleo de Bases de Conocimiento para mejorar su desempeño de distintos modos, e.g., detectando resultados incoherentes, proveyendo información a priori, reduciendo la complejidad de los algoritmos de inferencia probabilística, generando ejemplos de entrenamiento sintéticos, habilitando el aprendizaje a partir de experiencias pasadas, etc. El segundo grupo de contribuciones acomoda los resultados probabilísticos provenientes de los algoritmos de reconocimiento desarrollados en una nueva representación semántica, denominada Multiversal Semantic Map (MvSmap). Este mapa gestiona múltiples interpretaciones del espacio de trabajo del robot, llamadas universos, los cuales son anotados con la probabilidad de ser los correctos de acuerdo con el conocimiento actual del robot. Así, este enfoque proporciona una creencia fundamentada sobre la exactitud de la comprensión del robot sobre su entorno, lo que le permite operar de una manera más eficiente y coherente. Los algoritmos probabilísticos propuestos han sido testeados concienzudamente y comparados con otros enfoques actuales e innovadores empleando conjuntos de datos del estado del arte. De manera adicional, esta tesis también contribuye con dos conjuntos de datos, UMA-Offices and Robot@Home, los cuales contienen información sensorial capturada en distintos entornos de oficinas y casas, así como dos herramientas software, la librería Undirected Probabilistic Graphical Models in C++ (UPGMpp), y el conjunto de herramientas Object Labeling Toolkit (OLT), para el trabajo con Modelos Gráficos Probabilísticos y el procesamiento de conjuntos de datos respectivamente