Modélisation Planaire pour un RGB-D SLAM: Localisation éparse et Cartographie réduite

Abstract : This thesis deals with the Simultaneous Localisation and Mapping (SLAM) problem in indoor environments. In this context, we chose a visual SLAM using an RGB-D sensor (Kinect) to estimate the camera trajectory and to build a 3D map of the environment in realtime. Despite RGB-D cameras advantages (low cost, color images and depth maps), depth data resulting from this kind of sensors may be noisy, which affects pose estimation. In addition,due to points clouds sizes, the resulting global map is heavyweight and contains many redundant 3D points. In order to reduce the impact of these weaknesses on resolving the SLAM problem, we propose to use 3D planes, which are dominant in indoor scenes, for both camera poses estimations and 3D based-planes maps building process. Hence, 3D planes are used to generate 3D planar feature featuring less depth noise than the raw points extracted directly from points clouds. By regularizing depth values of raw 3D feature points belonging to these planes, we improve pose estimation when the scene is mainly composed of planes. Then, the detected 3D planes are used to build the global 3D map, creating a light representation of the environment based on these planes. The map is iteratively built from each new camera pose either by merging new planes to the existing ones or by adding new planes to the map. Thus,unlike conventional point-based representation, the size of the resulting 3D map is considerably reduced and the built map is more compact compared to point-based maps.These maps maybe used by mobile robotics and navigation applications. To show the benefits of our works, the conducted experiments to evaluate localisation accuracy, the influence of subsampled RGB-Ddata on plane detection, as well as quality of 3D plane-based maps against real scenes. Such plane-based maps represents a first step towards semantic maps.
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Hakim Elchaoui Elghor. Modélisation Planaire pour un RGB-D SLAM: Localisation éparse et Cartographie réduite. Traitement du signal et de l'image [eess.SP]. Université Paris-Saclay; Université Mohammed V - Rabat, 2016. Français. ⟨NNT : 2016SACLE054⟩. ⟨tel-01762162⟩

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