Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Automatic Reconstruction of Textured 3D Models

Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models

Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

UVC Dose Mapping by Mobile Robots

As infeções adquiridas em ambientes hospitalares são um problema persistente e crescente e a sua prevenção envolve a desinfeção de áreas e superfícies. A necessidade de métodos de desinfeção eficazes aumentou muito em consequência da pandemia de Covid-19. Um método eficaz é a utilização de exposição UVC porque a radiação UVC é absorvida pelos ácidos nucleicos e, portanto, é capaz de inativar microrganismos. Este método também traz muitas vantagens quando comparado com os métodos tradicionais de desinfeção. A desinfeção UVC pode ser realizada por equipamentos fixos que têm de ser deslocados de um local para outro de modo a desinfetar toda uma área, ou por um equipamento móvel autónomo que requer intervenção humana mínima para desinfetar completamente um ambiente. Esta dissertação foca em robôs móveis que desinfetam um ambiente utilizando radiação UVC. Estes robôs móveis são capazes de se mover autonomamente enquanto mapeiam o ambiente à sua volta e simultaneamente o desinfetam. Os robôs mantêm registo da dose aplicada a cada área do ambiente de modo a construir um mapa da dose e diferenciar as áreas completamente desinfetadas das que não o estão. Esta solução tem a vantagem de o robô realizar a desinfeção UVC sem necessitar de parar em cada área nem ter conhecimentos prévios sobre o ambiente. A validação desta solução foi realizada utilizando o rviz, uma ferramenta do Robot Operating System (ROS), e a LiDAR Camera L515. A câmara foi utilizada para recolher a informação necessária para a criação do mapa do ambiente e o rviz foi utilizado para visualizar o mapa da dose.Hospital-acquired infections are a persistent and increasing problem and their prevention involves disinfecting areas and surfaces. The necessity for effective disinfection methods has highly increased in consequence of the Covid-19 pandemic. An effective method is using UVC exposure because UVC radiation is absorbed by nucleic acids and, therefore, is able to inactivate microorganisms. This method also brings many advantages when compared with traditional disinfection methods. UVC disinfection can be performed by fixed equipments that have to be moved from place to place to disinfect an entire area, or by an autonomous mobile equipment that requires minimal human intervention to completely disinfect an environment. This dissertation is focused on mobile robots that disinfect an environment using UVC radiation. These mobile robots are able to move autonomously while mapping the surrounding environment and simultaneously disinfect it. The robots keep track of the dose applied to each area of the environment in order to build a dose map and differentiate areas that are completely disinfected from those that are not. This solution has the advantage of the robot performing UVC disinfection without needing to stop in each area nor having previous knowledge of the environment. The validation of this solution was performed using rviz, a Robot Operating System (ROS) tool, and the LiDAR Camera L515. The camera was used to capture the necessary information for creating the map of the environment and rviz was used to visualize the dose map