Nicola Fioraio

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— The availability of affordable RGB-D cameras like Microsoft Kinect can improve VSLAM applications, object 3D modeling and reconstruction of indoor environments, through the use of dense, synchronized depth and color images. The high frame rate of such devices isn't exploited so far, since they require both fast and accurate algorithms for real-time(More)
In this paper we propose a novel Semantic Bundle Adjustment framework whereby known rigid stationary objects are detected while tracking the camera and mapping the environment. The system builds on established tracking and mapping techniques to exploit incremental 3D reconstruction in order to validate hypotheses on the presence and pose of sought objects.(More)
Much recent progress has been made in the development of real-time, dense surface reconstruction algorithms that work with a single depth camera, such as the Microsoft Kinect. KinectFusion [3] demonstrated high-quality scanning of small environments, subsequently extended to large-scale reconstructions [2, 4]. A major barrier to complete reconstruction is(More)
— This work aims at automatic detection of man-made pole-like structures in scans of urban environments acquired by a 3D sensor mounted on top a moving vehicle. Pole-like structures, such as e.g. roadsigns and streetlights, are widespread in these environments, and their reliable detection is relevant to applications dealing with autonomous navigation,(More)
In this paper we present a new RGB-D SLAM system specifically designed for mobile platforms. Though the basic approach has already been proposed , many relevant changes are required to suit a user-centered mobile environment. In particular, our implementation tackles the strict memory constraints and limited computational power of a typical tablet device,(More)
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