Détection, localisation et suivi des obstacles et objets mobiles à partir d'une plate forme de stéréo-vision

Abstract : This PhD work is to be seen within the context of autonomous vehicle perception, in which the detection and localisation of elements of the surroundings in real time is an obvious requirement. Subsequent perception needs are manyfold, from localisation to obstacle detection, and are the subject of a continued research interest. The goal of this work is to build, in real time and from stereovision acquisition, a 3D map of the surroundings ; while detecting and tracking moving objects.Interest point selection and tracking on picture space are a first step, which we initiate by a thorough comparison of detectors from the literature. As regards tracking, we propose a massively parallel implementation of the standard KLT algorithm, using redundant tracking to provide reliable quality estimation. This allows us to track thousands of points in real-time, which compares favourably to the state of the art.Next step is the ego-motion estimation, along with the positioning of tracked points in 3D space. We first propose an iterative variant of the well known “SVD” process followed by UKF filtering, which allows for a very fast and reliable estimation. Then the position of every followed interest point is filtered on the fly over time, in contrast to most dense approaches from the literature.We finally propose a segmentation of moving objects in the augmented position-speed space, which is made possible by our continuous estimation of feature points position. Target tracking and filtering finally use a GM-PHD approach.
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Benjamin Lefaudeux. Détection, localisation et suivi des obstacles et objets mobiles à partir d'une plate forme de stéréo-vision. Autre. Ecole Nationale Supérieure des Mines de Paris, 2013. Français. ⟨NNT : 2013ENMP0091⟩. ⟨tel-01083731⟩

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