3 research outputs found

    Sigma-FP: Robot Mapping of 3D Floor Plans with an RGB-D Camera under Uncertainty

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    This work presents Sigma-FP, a novel 3D reconstruction method to obtain the floor plan of a multi-room environment from a sequence of RGB-D images captured by a wheeled mobile robot. For each input image, the planar patches of visible walls are extracted and subsequently characterized by a multivariate Gaussian distribution in the convenient Plane Parameter Space. Then, accounting for the probabilistic nature of the robot localization, we transform and combine the planar patches from the camera frame into a 3D global model, where the planar patches include both the plane estimation uncertainty and the propagation of the robot pose uncertainty. Additionally, processing depth data, we detect openings (doors and windows) in the wall, which are also incorporated in the 3D global model to provide a more realistic representation. Experimental results, in both real-world and synthetic environments, demonstrate that our method outperforms state-of-the art methods, both in time and accuracy, while just relying on Atlanta world assumption

    Efficient semantic place categorization by a robot through active line-of-sight selection

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    In this paper, we present an attention mechanism for mobile robots to face the problem of place categorization. Our approach, which is based on active perception, aims to capture images with characteristic or distinctive details of the environment that can be exploited to improve the efficiency (quickness and accuracy) of the place categorization. To do so, at each time moment, our proposal selects the most informative view by controlling the line-of-sight of the robot’s camera through a pan-only unit. We root our proposal on an information maximization scheme, formalized as a next-best-view problem through a Markov Decision Process (MDP) model. The latter exploits the short-time estimated navigation path of the robot to anticipate the next robot’s movements and make consistent decisions. We demonstrate over two datasets, with simulated and real data, that our proposal generalizes well for the two main paradigms of place categorization (object-based and image-based), outperforming typical camera-configurations (fixed and continuously-rotating) and a pure-exploratory approach, both in quickness and accuracy.This work was supported by the research projects WISER (DPI2017-84827-R) and ARPEGGIO (PID2020-117057), as well as by the Spanish grant program FPU19/00704. Funding for open access charge: Universidad de Málaga / CBUA
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