Localization is an important problem that must be resolved in order for a robot to make an estimation of its location based on observation and odometry updates. Relying on artificial landmarks such as the lines, circles, and goalposts in the robot soccer domain, current robot localization requires prior knowledge and suffers from uncertainty problems due to partial observation, and thus is less generalizable compared to human beings, who refer to their surroundings for complimentary information. To improve the certainty of the localization model, we propose a framework that recognizes orientation by actively using natural landmarks from the off-field surroundings, extracting these visual features from raw images. Our approach involves identifying visual features and natural landmarks, training with localization information to understand the surroundings, and prediction based on matching of features. This approach can increase the precision of robot orientation and improve localization accuracy by eliminating uncertain hypotheses, and in addition, it is also a general approach that can be extended and applied to other localization problems as well. / text

Made available in DSpace on 2014-12-17T14:56:01Z (GMT). No. of bitstreams: 1 ClauberGB.pdf: 726956 bytes, checksum: d3fb1b2d7c6ad784a1b7d40c1a54f8f8 (MD5) Previous issue date: 2004-03-08 / Several methods of mobile robot navigation request the mensuration of robot position and orientation in its workspace. In the wheeled mobile robot case, techniques based on odometry allow to determine the robot localization by the integration of incremental displacements of its wheels. However, this technique is subject to errors that accumulate with the distance traveled by the robot, making unfeasible its exclusive use. Other methods are based on the detection of natural or artificial landmarks present in the environment and whose location is known. This technique doesnt generate cumulative errors, but it can request a larger processing time than the methods based on odometry. Thus, many methods make use of both techniques, in such a way that the odometry errors are periodically corrected through mensurations obtained from landmarks. Accordding to this approach, this work proposes a hybrid localization system for wheeled mobile robots in indoor environments based on odometry and natural landmarks. The landmarks are straight lines de.ned by the junctions in environments floor, forming a bi-dimensional grid. The landmark detection from digital images is perfomed through the Hough transform. Heuristics are associated with that transform to allow its application in real time. To reduce the search time of landmarks, we propose to map odometry errors in an area of the captured image that possesses high probability of containing the sought mark / Diversos m?todos de navega??o de rob?s m?veis requerem a medi??o da posi??o e orienta??o do rob? no seu espa?o de trabalho. No caso de rob?s m?veis com rodas, t?cnicas baseadas em odometria permitem determinar a localiza??o do rob? atrav?s da integra??o de medi??es dos deslocamentos incrementais de suas rodas. No entanto, essa t?cnica est? sujeita a erros que se acumulam com a dist?ncia percorrida pelo rob?, o que inviabiliza o seu uso exclusivo. Outros m?todos se baseiam na detec??o de marcos naturais ou artificiais, cuja localiza??o ? conhecida, presentes no ambiente. Apesar desta t?cnica n?o gerar erros cumulativos, ela pode requisitar um tempo de processamento bem maior do que o uso de odometria. Assim, muitos m?todos fazem uso de ambas as t?cnicas, de modo a corrigir periodicamente os erros de odometria, atrav?s de medi??es obtidas a partir dos marcos. De acordo com esta abordagem, propomos neste trabalho um sistema h?brido de localiza??o para rob?s m?veis com rodas em ambientes internos, baseado em odometria e marcos naturais, onde os marcos adotados s?o linhas retas definidas pelas jun??es existentes no piso do ambiente, formando uma grade bi-dimensional no ch?o. Para a detec??o deste tipo de marco, a partir de imagens digitais, ? utilizada a transformada de Hough, associada a heur?sticas que permitem a sua aplica??o em tempo real. Em particular, para reduzir o tempo de busca dos marcos, propomos mapear erros de odometria em uma regi?o da imagem capturada que possua grande probabilidade de conter o marco procurado

In mobile robot applications, it is an important issue for a robot to know where it is. Accurate localization becomes crucial for navigation and map building applications because both route to follow and positions of the objects to be inserted into the map highly depend on the position of the robot in the environment. For localization, the robot uses the measurements that it takes by various devices such as laser rangefinders, sonars, odometry devices and vision. Generally these devices give the distances of the objects in the environment to the robot and proceesing these distance information, the robot finds its location in the environment. In this thesis, two vision-based robot localization algorithms are implemented. The first algorithm uses artificial landmarks as the objects around the robot and by measuring the positions of these landmarks with respect to the camera system, the robot locates itself in the environment. Locations of these landmarks are known. The second algorithm instead of using artificial landmarks, estimates its location by measuring the positions of the objects that naturally exist in the environment. These objects are treated as natural landmarks and locations of these landmarks are not known initially. A three-wheeled robot base on which a stereo camera system is mounted is used as the mobile robot unit. Processing and control tasks of the system is performed by a stationary PC. Experiments are performed on this robot system. The stereo camera system is the measurement device for this robot.

Submitted by Fernando Souza (fernandoafsou@gmail.com) on 2017-08-10T11:47:56Z No. of bitstreams: 1 arquivototal.pdf: 11498612 bytes, checksum: 9182e9402b0905c4209bc405c726f8cc (MD5) / Made available in DSpace on 2017-08-10T11:47:56Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 11498612 bytes, checksum: 9182e9402b0905c4209bc405c726f8cc (MD5) Previous issue date: 2017-02-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / With the advance in the research of mobile robots localization algorithms, the need for natural landmark identification and recognition has increased. The detection of natural landmarks is a challenging task because their appearance can be different in shape and design and, as well, they suffer influence of the environment illumination. As an example, a typical 2D object recognition algorithm may not be able to handle the large optical variety of doors and staircases in large corridors. On another direction, recent improvements in low-cost 3D sensors (of the type RGB-D) enable robots to perceive the environment as a 3D spatial structure. Thus, using this new technology, an algorithm for natural landmark identification and recognition based on images acquired from an RGB-D camera is proposed. Basically, during the identification phase that is a first step for working with landmarks, the algorithm exploits the basic structural knowledge about the landmarks by extracting their edges and creating a cloud of edge points. In the next, the recognition phase, the edges are used with a proposed on-the-fly unsupervised recognition algorithm in order to demonstrate the effectiveness of the approach in recognizing doors and staircases. Two methods of recognition have been proposed and results show that a general technique of the two methods passes from the 96 of accuracy. Future approaches propose a mix of these two methods for better results of recognition, as well as inclusion of new objects such as drinking fountains, dumps and compare this modified approach with other approaches that require training, such as nearest K-neighbors, Bayes and neural networks . / Com o avanço na pesquisa de algoritmos de localização de robôs móveis, a necessidade de identificação e reconhecimento de pontos de referência naturais aumentou. A detecção de marcos naturais é uma tarefa desafiadora, porque a sua aparência pode ser diferente em forma e design e, também, eles sofrem influência da iluminação do ambiente. Como um exemplo, um algoritmo de reconhecimento de objeto 2D típico pode não ser capaz de lidar com a grande variedade óptica de portas e escadas em corredores grandes. Em outra direção, as melhorias recentes em sensores 3D de baixo custo (do tipo RGB-D) permitem aos robôs perceber o ambiente como uma estrutura espacial 3D. Assim, usando esta nova tecnologia, um algoritmo para identificação e reconhecimento de marco natural baseado em imagens adquiridas a partir de uma câmera RGB-D é proposto. Basicamente, durante a fase de identificação que é um primeiro passo para trabalhar com marcos, o algoritmo explora o conhecimento estrutural básico sobre os pontos de referência, extraindo suas bordas e criando uma nuvem de pontos de borda. No próxima, a fase de reconhecimento, as arestas são usadas com um algoritmo de reconhecimento não supervisionado proposto on-the-fly para demonstrar a eficácia da abordagem no reconhecimento de portas e escadarias. Dois métodos de Reconhecimento foram propostos e resultados mostram que a eficiência geral dos dois métodos passa dos 96% de Precisão de reconhecimento. Abordagens futuras propõem-se a fusão dos dois métodos para melhores resultados no reconhecimento, bem como inclusão de novos objetos como bebedouros, lixeiras e comparar essa abordagem modificada com outras abordagens que necessitam de treinamento, como K-Neighbouring mais próximo, Bayes e redes neurais.