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)
Depth cameras have helped commoditize 3D digitization of the real-world. It is now feasible to use a single Kinect-like camera to scan in an entire building or other large-scale scenes. At large scales, however, there is an inherent challenge of dealing with distortions and drift due to accumulated pose estimation errors. Existing techniques suffer from one(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)
We propose an effective, real-time solution to the RGB-D SLAM problem dubbed SlamDunk. Our proposal features a multi-view camera tracking approach based on a dynamic local map of the workspace, enables metric loop closure seamlessly and preserves local consistency by means of relative bundle adjustment principles. SlamDunk requires a few threads, low memory(More)
In this paper we present a pipeline for automatic detection of traffic signs in images. The proposed system can deal with high appearance variations, which typically occur in traffic sign recognition applications, especially with strong illumination changes and dramatic scale changes. Unlike most existing systems, our pipeline is based on interest regions(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)
Simultaneous Localization and Mapping (SLAM) algorithms have been recently deployed on mobile devices, where they can enable a broad range of novel applications. Nevertheless, pure visual SLAM is inherently weak at operating in environments with a reduced number of visual features. Indeed, even many recent proposals based on RGB-D sensors cannot handle(More)
In this paper we propose an extension to the KinectFusion approach which enables both SLAM-graph optimization, usually required on large looping routes, as well as discovery of semantic information in the form of object detection and localization. Global optimization is achieved by incorporating the notion of keyframe into a KinectFusion-style approach,(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. road signs and streetlights, are widespread in these environments, and their reliable detection is relevant to applications dealing with autonomous navigation,(More)