Vision-based estimation of altitude from aerial images

One of the wide engineering fields is aircraft technologies and one of the most common needs for Airplane or UAV is estimating the altitude, which is some time difficult to estimate due to weather fluctuations and instability of the main parameters like pressure and speed. However, a combination of different sensors has been used to estimate altitude to guarantee an accurate reading and it is the method used these days. To overcome this problem is to use more capable technology such as machine vision based system to estimate the altitude, as advantages light weight, intelligence and accuracy, cheaper than commercial sensors as well as, computationally inexpensive. In this paper, we propose a vision-based system that can perform altitude estimation from aerial images. The satisfactory experimental results demonstrate the effectiveness of the proposed system

Autonomous visual-inertial navigation and absolute visual scale estimation

In this thesis, we present a system that uses a single camera and an inertial measurement unit (IMU) to navigate an Unmanned Aerial Vehicle (UAV) in a previously unknown environment. The approach consists of two parts. First, we apply a state-of-the-art simultaneous localization and mapping (SLAM) method to the video stream of a onboard camera. From the SLAM system, an up-to-a-scale pose of the camera is estimated, because the absolute size of the environment cannot be estimated with a single camera. Second, the estimated pose is fused with the data from IMU to resolve the scale ambiguity. While analyzing the performance of the system, we find that the conver- gence rate of scale decreases when the magnitude of scale increases. This relationship has not been demonstrated and explained before. In this thesis, we present an analysis and explanation of this phenomenon

Inertial Sensed Ego-motion for 3D Vision

Inertial sensors attached to a camera can provide valuable data about camera pose and movement. In biological vision systems, inertial cues provided by the vestibular system are fused with vision at an early processing stage. In this article we set a framework for the combination of these two sensing modalities. Cameras can be seen as ray direction measuring devices, and in the case of stereo vision, depth along the ray can also be computed. The ego-motion can be sensed by the inertial sensors, but there are limitations determined by the sensor noise level. Keeping track of the vertical direction is required, so that gravity acceleration can be compensated for, and provides a valuable spatial reference. Results are shown of stereo depth map alignment using the vertical reference. The depth map points are mapped to a vertically aligned world frame of reference. In order to detect the ground plane, a histogram is performed for the different heights. Taking the ground plane as a reference plane for the acquired maps, the fusion of multiple maps reduces to a 2D translation and rotation problem. The dynamic inertial cues can be used as a first approximation for this transformation, allowing a fast depth map registration method. They also provide an image independent location of the image focus of expansion and center of rotation useful during visual based navigation tasks. © 2004 Wiley Periodicals, Inc

Perceção e arquitectura de software para robótica móvel

Doutoramento em Ciências da ComputaçãoWhen developing software for autonomous mobile robots, one has to inevitably tackle some kind of perception. Moreover, when dealing with agents that possess some level of reasoning for executing their actions, there is the need to model the environment and the robot internal state in a way that it represents the scenario in which the robot operates. Inserted in the ATRI group, part of the IEETA research unit at Aveiro University, this work uses two of the projects of the group as test bed, particularly in the scenario of robotic soccer with real robots. With the main objective of developing algorithms for sensor and information fusion that could be used e ectively on these teams, several state of the art approaches were studied, implemented and adapted to each of the robot types. Within the MSL RoboCup team CAMBADA, the main focus was the perception of ball and obstacles, with the creation of models capable of providing extended information so that the reasoning of the robot can be ever more e ective. To achieve it, several methodologies were analyzed, implemented, compared and improved. Concerning the ball, an analysis of ltering methodologies for stabilization of its position and estimation of its velocity was performed. Also, with the goal keeper in mind, work has been done to provide it with information of aerial balls. As for obstacles, a new de nition of the way they are perceived by the vision and the type of information provided was created, as well as a methodology for identifying which of the obstacles are team mates. Also, a tracking algorithm was developed, which ultimately assigned each of the obstacles a unique identi er. Associated with the improvement of the obstacles perception, a new algorithm of estimating reactive obstacle avoidance was created. In the context of the SPL RoboCup team Portuguese Team, besides the inevitable adaptation of many of the algorithms already developed for sensor and information fusion and considering that it was recently created, the objective was to create a sustainable software architecture that could be the base for future modular development. The software architecture created is based on a series of di erent processes and the means of communication among them. All processes were created or adapted for the new architecture and a base set of roles and behaviors was de ned during this work to achieve a base functional framework. In terms of perception, the main focus was to de ne a projection model and camera pose extraction that could provide information in metric coordinates. The second main objective was to adapt the CAMBADA localization algorithm to work on the NAO robots, considering all the limitations it presents when comparing to the MSL team, especially in terms of computational resources. A set of support tools were developed or improved in order to support the test and development in both teams. In general, the work developed during this thesis improved the performance of the teams during play and also the e ectiveness of the developers team when in development and test phases.Durante o desenvolvimento de software para robôs autónomos móveis, e inevitavelmente necessário lidar com algum tipo de perceção. Al em disso, ao lidar com agentes que possuem algum tipo de raciocínio para executar as suas ações, há a necessidade de modelar o ambiente e o estado interno do robô de forma a representar o cenário onde o robô opera. Inserido no grupo ATRI, integrado na unidade de investigação IEETA da Universidade de Aveiro, este trabalho usa dois dos projetos do grupo como plataformas de teste, particularmente no cenário de futebol robótico com robôs reais. Com o principal objetivo de desenvolver algoritmos para fusão sensorial e de informação que possam ser usados eficazmente nestas equipas, v arias abordagens de estado da arte foram estudadas, implementadas e adaptadas para cada tipo de robôs. No âmbito da equipa de RoboCup MSL, CAMBADA, o principal foco foi a perceção da bola e obstáculos, com a criação de modelos capazes de providenciar informação estendida para que o raciocino do robô possa ser cada vez mais eficaz. Para o alcançar, v arias metodologias foram analisadas, implementadas, comparadas e melhoradas. Em relação a bola, foi efetuada uma análise de metodologias de filtragem para estabilização da sua posição e estimação da sua velocidade. Tendo o guarda-redes em mente, foi também realizado trabalho para providenciar informação de bolas no ar. Quanto aos obstáculos, foi criada uma nova definição para a forma como são detetados pela visão e para o tipo de informação fornecida, bem como uma metodologia para identificar quais dos obstáculos são colegas de equipa. Além disso foi desenvolvido um algoritmo de rastreamento que, no final, atribui um identicador único a cada obstáculo. Associado a melhoria na perceção dos obstáculos foi criado um novo algoritmo para realizar desvio reativo de obstáculos. No contexto da equipa de RoboCup SPL, Portuguese Team, al em da inevitável adaptação de vários dos algoritmos j a desenvolvidos para fusão sensorial e de informação, tendo em conta que foi recentemente criada, o objetivo foi criar uma arquitetura sustentável de software que possa ser a base para futuro desenvolvimento modular. A arquitetura de software criada e baseada numa série de processos diferentes e métodos de comunicação entre eles. Todos os processos foram criados ou adaptados para a nova arquitetura e um conjunto base de papeis e comportamentos foi definido para obter uma framework funcional base. Em termos de perceção, o principal foco foi a definição de um modelo de projeção e extração de pose da câmara que consiga providenciar informação em coordenadas métricas. O segundo objetivo principal era adaptar o algoritmo de localização da CAMBADA para funcionar nos robôs NAO, considerando todas as limitações apresentadas quando comparando com a equipa MSL, principalmente em termos de recursos computacionais. Um conjunto de ferramentas de suporte foram desenvolvidas ou melhoradas para auxiliar o teste e desenvolvimento em ambas as equipas. Em geral, o trabalho desenvolvido durante esta tese melhorou o desempenho da equipas durante os jogos e também a eficácia da equipa de programação durante as fases de desenvolvimento e teste