Probabilistic qualitative spatial reasoning for image interpretation.

Um sistema artificial pode usar raciocínio espacial qualitativo para inferir informações sobre seu ambiente tridimensional a partir de imagens bidimensionais. Inferências realizadas com base em raciocínio espacial qualitativo devem ser capazes de lidar com incertezas. Neste trabalho investigamos a utilização de técnicas probabilísticas para tornar o raciocínio espacial qualitativo mais robusto a incertezas e aplicável a agentes móveis em ambientes reais. Investigamos uma formalização de raciocínio espacial com lógica de descrição probabilística em um subdomínio de tráfego. Desenvolvemos também um método que combina raciocínio espacial qualitativo com um filtro Bayesiano para desenvolver dois sistemas que foram aplicados na auto localização de um robô móvel. Executamos dois experimentos de auto localização; um utilizando a teoria de relações qualitativas percebíveis sobre sombra com filtro Bayesiano; e outro utilizando o cálculo de oclusão de regiões e o cálculo de direção com filtro Bayesiano. Ambos os sistemas obtiveram resultados positivos onde somente o raciocínio espacial qualitativo não foi capaz de inferir a localização do robô. Os experimentos com dados reais mostraram robustez aos ruídos e à informação parcial.An artificial system can use qualitative spatial reasoning to obtain information about its tridimensional environment, from bi-dimensional images. Inferences produced by qualitative spatial reasoning must be able to deal with uncertainty. This work investigates the use of probabilistic techniques to make qualitative spatial reasoning more robust against uncertainty, and better applicable to mobile agents in real environments. The work investigates a formalization of spatial reasoning using probabilistic description logics in a traffic domain. Additionally, a method is presented that combines qualitative spatial reasoning with a Bayesian filter, to develop two systems that are applied to self-localization of mobile robots. Two experiments are described; one using the theory of perceptual qualitative relations about shadows; the other using occlusion calculus and direction calculus. Both systems are combined with a Bayesian filter producing positive results in situations where qualitative spatial reasoning alone cannot infer robot location. Experiments with real data show robustness to noise and partial information

Reasoning about shadows in a mobile robot environment

This paper describes a logic-based formalism for\ud qualitative spatial reasoning with cast shadows (Perceptual\ud Qualitative Relations on Shadows, or PQRS) and presents\ud results of a mobile robot qualitative self-localisation experiment\ud using this formalism. Shadow detection was accomplished\ud by mapping the images from the robot’s monocular\ud colour camera into a HSV colour space and then thresholding\ud on the V dimension. We present results of selflocalisation\ud using two methods for obtaining the threshold\ud automatically: in one method the images are segmented\ud according to their grey-scale histograms, in the other, the\ud threshold is set according to a prediction about the robot’s\ud location, based upon a qualitative spatial reasoning theory\ud about shadows. This theory-driven threshold search and the\ud qualitative self-localisation procedure are the main contributions\ud of the present research. To the best of our knowledge\ud this is the first work that uses qualitative spatial representations\ud both to perform robot self-localisation and to calibrate\ud a robot’s interpretation of its perceptual input.Paulo Santos acknowledges support from FAPESP project 2012/04089-3, São Paulo and bolsa PQ, CNPq 303331/2011-9 ; Hannah Dee acknowledges support from EPSRC project LAVID, EP/D061334/1, UK; Valquiria Fenelon is a graduate student sponsored by CAPES, Brazil; Fabio Cozman acknowledges FAPESP and bolsa PQ, CNPq 305395/2010-