Hybrid mapping for static and non-static indoor environments

Mención Internacional en el título de doctorIndoor environments populated by humans, such as houses, offices or universities, involve a great complexity due to the diversity of geometries and situations that they may present. Apart from the size of the environment, they can contain multiple rooms distributed into floors and corridors, repetitive structures and loops, and they can get as complicated as one can imagine. In addition, the structure and situations that the environment present may vary over time as objects could be moved, doors can be frequently opened or closed and places can be used for different purposes. Mobile robots need to solve these challenging situations in order to successfully operate in the environment. The main tools that a mobile robot has for dealing with these situations relate to navigation and perception and comprise mapping, localization, path planning and map adaptation. In this thesis, we try to address some of the open problems in robot navigation in non-static indoor environments. We focus on house-like environments as the work is framed into the HEROITEA research project that aims attention at helping elderly people with their everyday-life activities at their homes. This thesis contributes to HEROITEA with a complete robotic mapping system and map adaptation that grants safe navigation and understanding of the environment. Moreover, we provide localization and path planning strategies within the resulting map to further operate in the environment. The first problem tackled in this thesis is robot mapping in static indoor environments. We propose a hybrid mapping method that structures the information gathered from the environment into several maps. The hybrid map contains diverse knowledge of the environment such as its structure, the navigable and blocked paths, and semantic knowledge, such as the objects or scenes in the environment. All this information is separated into different components of the hybrid map that are interconnected so the system can, at any time, benefit from the information contained in every component. In addition to the conceptual conception of the hybrid map, we have also developed building procedures and an exploration algorithm to autonomous build the hybrid map. However, indoor environments populated by humans are far from being static as the environment may change over time. For this reason, the second problem tackled in this thesis is the adaptation of the map to non-static environments. We propose an object-based probabilistic map adaptation that calculates the likelihood of moving or remaining in its place for the different objects in the environment. Finally, a map is just a description of the environment whose importance is mostly related to how the map is used. In addition, map representations are more valuable as long as they offer a wider range of applications. Therefore, the third problem that we approach in this thesis is exploiting the intrinsic characteristics of the hybrid map in order to enhance the performance of localization and path planning methods. The particular objectives of these approaches are precision for robot localization and efficiency for path planning in terms of execution time and traveled distance. We evaluate our proposed methods in a diversity of simulated and real-world indoor environments. In this extensive evaluation, we show that hybrid maps can be efficiently built and maintained over time and they open up for new possibilities for localization and path planning. In this thesis, we show an increase in localization precision and robustness and an improvement in path planning performance. In sum, this thesis makes several contributions in the context of robot navigation in indoor environments, and especially in hybrid mapping. Hybrid maps offer higher efficiency during map building and other applications such as localization and path planning. In addition, we highlight the necessity of dealing with the dynamics of indoor environments and the benefits of combining topological, semantic and metric information to the autonomy of a mobile robot.Los entornos de interiores habitados por personas, como casas, oficinas o universidades, entrañan una gran complejidad por la diversidad de geometrías y situaciones que pueden ocurrir. Aparte de las diferencias en tamaño, estos entornos pueden contener muchas habitaciones organizadas en diferentes plantas o pasillos, pueden presentar estructuras repetitivas o bucles de tal forma que los entornos pueden llegar a ser tan complejos como uno se pueda imaginar. Además, la estructura y el estado del entorno pueden variar con el tiempo, ya que los objetos pueden moverse, las puertas pueden estar cerradas o abiertas y diferentes espacios pueden ser usados para diferentes propósitos. Los robots móviles necesitan resolver estas situaciones difíciles para poder funcionar de una forma satisfactoria. Las principales herramientas que tiene un robot móvil para manejar estas situaciones están relacionadas con la navegación y la percepción y comprenden el mapeado, la localización, la planificación de trayectorias y la adaptación del mapa. En esta tesis, abordamos algunos de los problemas sin resolver de la navegación de robots móviles en entornos de interiores no estáticos. Nos centramos en entornos tipo casa ya que este trabajo se enmarca en el proyecto de investigación HEROITEA que se enfoca en ayudar a personas ancianas en tareas cotidianas del hogar. Esta tesis contribuye al proyecto HEROITEA con un sistema completo de mapeado y adaptación del mapa que asegura una navegación segura y la comprensión del entorno. Además, aportamos métodos de localización y planificación de trayectorias usando el mapa construido para realizar nuevas tareas en el entorno. El primer problema que se aborda en esta tesis es el mapeado de entornos de interiores estáticos por parte de un robot. Proponemos un método de mapeado híbrido que estructura la información capturada en varios mapas. El mapa híbrido contiene información sobre la estructura del entorno, las trayectorias libres y bloqueadas y también incluye información semántica, como los objetos y escenas en el entorno. Toda esta información está separada en diferentes componentes del mapa híbrido que están interconectados de tal forma que el sistema puede beneficiarse en cualquier momento de la información contenida en cada componente. Además de la definición conceptual del mapa híbrido, hemos desarrollado unos procedimientos para construir el mapa y un algoritmo de exploración que permite que esta construcción se realice autónomamente. Sin embargo, los entornos de interiores habitados por personas están lejos de ser estáticos ya que pueden cambiar a lo largo del tiempo. Por esta razón, el segundo problema que intentamos solucionar en esta tesis es la adaptación del mapa para entornos no estáticos. Proponemos un método probabilístico de adaptación del mapa basado en objetos que calcula la probabilidad de que cada objeto en el entorno haya sido movido o permanezca en su posición anterior. Para terminar, un mapa es simplemente una descripción del entorno cuya importancia está principalmente relacionada con su uso. Por ello, los mapas más valiosos serán los que ofrezcan un rango mayor de aplicaciones. Para abordar este asunto, el tercer problema que intentamos solucionar es explotar las características intrínsecas del mapa híbrido para mejorar el desempeño de métodos de localización y de planificación de trayectorias usando el mapa híbrido. El objetivo principal de estos métodos es aumentar la precisión en la localización del robot y la eficiencia en la planificación de trayectorias en relación al tiempo de ejecución y la distancia recorrida. Hemos evaluado los métodos propuestos en una variedad de entornos de interiores simulados y reales. En esta extensa evaluación, mostramos que los mapas híbridos pueden construirse y mantenerse en el tiempo de forma eficiente y que dan lugar a nuevas posibilidades en cuanto a localización y planificación de trayectorias. En esta tesis, mostramos un aumento en la precisión y robustez en la localización y una mejora en el desempeño de la planificación de trayectorias. En resumen, esta tesis lleva a cabo diversas contribuciones en el ámbito de la navegación de robots móviles en entornos de interiores, y especialmente en mapeado híbrido. Los mapas híbridos ofrecen más eficiencia durante la construcción del mapa y en otras tareas como la localización y la planificación de trayectorias. Además, resaltamos la necesidad de tratar los cambios en entornos de interiores y los beneficios de combinar información topológica, semántica y métrica para la autonomía del robot.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Javier González Jiménez.- Vocal: Nancy Marie Amat

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