Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Design and implementation of a relative localization system for ground and aerial robotic teams

The main focus of this thesis is to address the relative localization problem of a heterogenous team which comprises of both ground and micro aerial vehicle robots. This team configuration allows to combine the advantages of increased accessibility and better perspective provided by aerial robots with the higher computational and sensory resources provided by the ground agents, to realize a cooperative multi robotic system suitable for hostile autonomous missions. However, in such a scenario, the strict constraints in flight time, sensor pay load, and computational capability of micro aerial vehicles limits the practical applicability of popular map-based localization schemes for GPS denied navigation. Therefore, the resource limited aerial platforms of this team demand simpler localization means for autonomous navigation. Relative localization is the process of estimating the formation of a robot team using the acquired inter-robot relative measurements. This allows the team members to know their relative formation even without a global localization reference, such as GPS or a map. Thus a typical robot team would benefit from a relative localization service since it would allow the team to implement formation control, collision avoidance, and supervisory control tasks, independent of a global localization service. More importantly, a heterogenous team such as ground robots and computationally constrained aerial vehicles would benefit from a relative localization service since it provides the crucial localization information required for autonomous operation of the weaker agents. This enables less capable robots to assume supportive roles and contribute to the more powerful robots executing the mission. Hence this study proposes a relative localization-based approach for ground and micro aerial vehicle cooperation, and develops inter-robot measurement, filtering, and distributed computing modules, necessary to realize the system. The research study results in three significant contributions. First, the work designs and validates a novel inter-robot relative measurement hardware solution which has accuracy, range, and scalability characteristics, necessary for relative localization. Second, the research work performs an analysis and design of a novel nonlinear filtering method, which allows the implementation of relative localization modules and attitude reference filters on low cost devices with optimal tuning parameters. Third, this work designs and validates a novel distributed relative localization approach, which harnesses the distributed computing capability of the team to minimize communication requirements, achieve consistent estimation, and enable efficient data correspondence within the network. The work validates the complete relative localization-based system through multiple indoor experiments and numerical simulations. The relative localization based navigation concept with its sensing, filtering, and distributed computing methods introduced in this thesis complements system limitations of a ground and micro aerial vehicle team, and also targets hostile environmental conditions. Thus the work constitutes an essential step towards realizing autonomous navigation of heterogenous teams in real world applications

360º Indoors image processing for 3D model reconstruction

In this modern age of computer technology we are pushing the unimaginable limits of our reality. One of the human desires with these advances is to digitise the vast amount of information that is present in our reality. An important source of information is the 3-dimensional space in which we live. Especially indoors environments that we frequently occupy, for example, living places. With the proliferation of photographing devices, the development of cheap omnidirectional cameras has been one of the interests. So nowadays it is quite easy to obtain spatial data of interior spaces in form of equirectangular images. In this project we study the problem of 3D Indoors Model Reconstruction from Spherical Images. Though, we study it under perspective based methods as it is possible to perform the conversion from one to other. We first formally specify the problem to be solved. We find many different specifications and describe reconstruction methods for some of them. We choose one specification for our use case. Most of the methods require feature extraction and matching, and then performing multi-view geometry estimation. We continue the study of these methods in the experimentation phase. We propose different hypothesis relevant to different steps, perform experiments and form our conclusions. We finish our work by implementing a very simple system solving this problem, making use of ASIFT feature extractor, FLANN kD-Tree feature matcher, and OpenCV's essential matrix estimation algorithm.En aquesta era moderna de la tecnologia de computadors estem empenyent els líımits inimaginables de la nostra realitat. Una de les aspiracions humanes amb aquests avenços és la digitalització de l’enorme quantitat d’informació present en la nostra realitat. Una de les fonts importants d’informació és l’espai 3-dimensional en el que vivim. Especialment, els entorns interiors que habitem, per exemple, els habitatges. Amb la proliferació dels dispositius fotogràfics, el desenvolupament de càmeres omnidireccionals barates ha estat un dels interessos. Per aquest motiu, avui en dia és molt fàcil obtenir dades espacials dels espais interiors en forma d’imatges equirectangulars. En aquest projecte estudiem el problema de la Reconstrucció de Models 3D d’Interiors a partir d’Imatges Esfèriques. Tanmateix, estudiem el problema fent ús de mètodes basats en la perspectiva ja que és possible fer la conversió d’un a l’altre. En primer lloc, especifiquem formalment el problema a resoldre. A continuació, trobem diverses especificacions i descrivim mètodes de reconstruccions per algunes d’elles. Seleccionem una especificaciò pel nostre cas d’ús. La majoria de mètodes utilitzen feature extraction, feature matching i epipolar geometry. Continuem l’estudi amb la fase d’experimentació. Proposem hipòtesis rellevants a diferents passos, realitzem els experiments i extraiem conclusions. Acabem el treball implementant un sistema simple resolent el problema, fent ús de ASIFT feature extractor, FLANN kD- Tree feature matcher, i l’algorisme d’OpenCV per l’aproximació de la matriu essencialEn esta era moderna de tecnología de computadores estamos empujando los límites inimaginables de nuestra realidad. Una de las aspiraciones humanas con estos avances es la digitalización de la tremenda cantidad de información presente en nuestra realidad. Una de las importantes fuentes de información es el espacio 3-dimensional en que vivimos. Especialmente los entornos interiores que habitamos, por ejemplo, las viviendas. Con la proliferación de los dispositivos fotográficos, el desarrollo de cámaras omnidireccionales baratas ha sido uno de los intereses. Por ello, hoy en día es muy fácil de obtener datos espaciales de los espacios interiores en forma de imágenes equirectangulares. En este proyecto estudiamos el problema de Reconstrucción de Modelos 3D de Interiores desde Imágenes Esféricas. Sin embargo, estudiamos el problema bajo métodos basados en la perspectiva ya que es posible hacer la conversión de uno al otro. Primero especificamos formalmente el problema a resolver. Encontramos distintas especificaciones y describimos métodos de reconstruccion para algunas de ellas. Seleccionamos una especificación para nuestro caso de uso. La mayoría de métodos utilizan feature extraction, feature matching, y epipolar geometry. Continuamos el estudio en la fase de experimentación. Proponemos hipótesis relevantes a diferentes pasos, realizamos los experimentos y sacamos conclusiones. Acabamos el trabajo implementando un sistema simple resolviendo el problema, haciendo uso de ASIFT feature extractor, FLANN kD-Tree feature matcher, y el algoritmo de OpenCV para la aproximación de la matriz esencial