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    Wyświetlanie 1-4 z 4
    Tytuł:
    RGB-D terrain perception and dense mapping for legged robots
    Autorzy:
    Belter, D.
    Łabecki, P
    Fankhauser, P.
    Siegwart, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/330144.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    RGB-D perception
    elevation mapping
    uncertainty
    legged robots
    RGB-D
    niepewność
    robot nożny
    Opis:
    This paper addresses the issues of unstructured terrain modeling for the purpose of navigation with legged robots. We present an improved elevation grid concept adopted to the specific requirements of a small legged robot with limited perceptual capabilities. We propose an extension of the elevation grid update mechanism by incorporating a formal treatment of the spatial uncertainty. Moreover, this paper presents uncertainty models for a structured light RGB-D sensor and a stereo vision camera used to produce a dense depth map. The model for the uncertainty of the stereo vision camera is based on uncertainty propagation from calibration, through undistortion and rectification algorithms, allowing calculation of the uncertainty of measured 3D point coordinates. The proposed uncertainty models were used for the construction of a terrain elevation map using the Videre Design STOC stereo vision camera and Kinect-like range sensors. We provide experimental verification of the proposed mapping method, and a comparison with another recently published terrain mapping method for walking robots.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 81-97
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient generation of 3D surfel maps using RGB-D sensors
    Autorzy:
    Wilkowski, A.
    Kornuta, T.
    Stefańczyk, M.
    Kasprzak, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/331116.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    RGB-D sensor
    V-SLAM
    surfel map
    frustum culling
    octree
    czujnik RGB-D
    drzewo ósemkowe
    Opis:
    The article focuses on the problem of building dense 3D occupancy maps using commercial RGB-D sensors and the SLAM approach. In particular, it addresses the problem of 3D map representations, which must be able both to store millions of points and to offer efficient update mechanisms. The proposed solution consists of two such key elements, visual odometry and surfel-based mapping, but it contains substantial improvements: storing the surfel maps in octree form and utilizing a frustum culling-based method to accelerate the map update step. The performed experiments verify the usefulness and efficiency of the developed system.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 99-122
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Utilizing relevant RGB-D data to help recognize RGB images in the target domain
    Autorzy:
    Gao, Depeng
    Liu, Jiafeng
    Wu, Rui
    Cheng, Dansong
    Fan, Xiaopeng
    Tang, Xianglong
    Powiązania:
    https://bibliotekanauki.pl/articles/329725.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    object recognition
    RGB-D image
    transfer learning
    privileged information
    rozpoznawanie obiektu
    obraz RGB-D
    uczenie maszynowe
    informacja poufna
    Opis:
    With the advent of 3D cameras, getting depth information along with RGB images has been facilitated, which is helpful in various computer vision tasks. However, there are two challenges in using these RGB-D images to help recognize RGB images captured by conventional cameras: one is that the depth images are missing at the testing stage, the other is that the training and test data are drawn from different distributions as they are captured using different equipment. To jointly address the two challenges, we propose an asymmetrical transfer learning framework, wherein three classifiers are trained using the RGB and depth images in the source domain and RGB images in the target domain with a structural risk minimization criterion and regularization theory. A cross-modality co-regularizer is used to restrict the two-source classifier in a consistent manner to increase accuracy. Moreover, an L2,1 norm cross-domain co-regularizer is used to magnify significant visual features and inhibit insignificant ones in the weight vectors of the two RGB classifiers. Thus, using the cross-modality and cross-domain co-regularizer, the knowledge of RGB-D images in the source domain is transferred to the target domain to improve the target classifier. The results of the experiment show that the proposed method is one of the most effective ones.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 611-621
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient RGB-D data processing for feature-based self-localization of mobile robots
    Autorzy:
    Kraft, M.
    Nowicki, M.
    Penne, R.
    Schmidt, A.
    Skrzypczyński, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/330295.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    visual odometry
    simultaneous localization
    simultaneous mapping
    RGB-D
    tracking
    point features
    odometria wizyjna
    lokalizacja jednoczesna
    śledzenie
    Opis:
    The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees of freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This work focuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a single RGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previous results as to the general architecture, the important novel elements introduced here are aimed at improving the precision and robustness of the motion estimate computed from the matching point features of two RGB-D frames. Moreover, we demonstrate that the visual odometry system can serve as the front-end for a pose-based simultaneous localization and mapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientifically verifiable. The experimental results demonstrate gains due to the improved feature extraction and management mechanisms, whereas the performance of the whole navigation system compares favorably to results known from the literature.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 63-79
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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