Отрывок: IV Международная конференция и молодёжная школа «Информационные технологии и нанотехнологии» (ИТНТ-2018) 1288 Figure 1. Pair of frames. Figure 2. Point cloud from the first frame. Note that by applying the ICP algorithm to sub-clouds a set of transformation matrices are obtained. The transformation matrices could be used in different ways for reconstruction of the 3D dense map of the environment: each matrix tforms_i from ICP algorithm to each i sub-cloud, or tform_1 for all sub-clouds. ...
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dc.contributor.authorRuchay, A.N.-
dc.contributor.authorDorofeev, K.A.-
dc.contributor.authorKober, A.V.-
dc.date.accessioned2018-05-15 13:23:22-
dc.date.available2018-05-15 13:23:22-
dc.date.issued2018-
dc.identifierDspace\SGAU\20180514\69219ru
dc.identifier.citationRuchay A.N. Accurate reconstruction of the 3D indoor environment map with a RGB-D camera based on multiple ICP / A.N. Ruchay, K.A. Dorofeev, A.V. Kober // Сборник трудов IV международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2018) - Самара: Новая техника, 2018. - С.1286-1293ru
dc.identifier.urihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Accurate-reconstruction-of-the-3D-indoor-environment-map-with-a-RGBD-camera-based-on-multiple-ICP-69219-
dc.descriptionОсновная статьяru
dc.description.abstractIn this paper, we propose a new method for 3D map reconstruction using the Kinect sensor based on multiple ICP. The Kinect sensor provides RGB images as well as depth images. Since the depth and RGB color images are captured by one Kinect sensor with multiple views, each depth image should be related to the color image. After matching of the images (registration), point-topoint corresponding between two depth images is found, and they can be combined and represented in the 3D space. In order to obtain a dense 3D map of the 3D indoor environment, we design an algorithm to combine information from multiple views of the Kinect sensor. First, features extracted from color and depth images are used to localize them in a 3D scene. Next, Iterative Closest Point (ICP) algorithm is used to align all frames. As a result, a new frame is added to the dense 3D model. However, the spatial distribution and resolution of depth data affect to the performance of 3D scene reconstruction system based on ICP. In this paper we automatically divide the depth data into sub-clouds with similar resolution, to align them separately, and unify in the entire points cloud. This method is called the multiple ICP. The presented computer simulation results show an improvement in accuracy of 3D map reconstruction using real data.ru
dc.language.isoenru
dc.publisherНовая техникаru
dc.subject3D reconstruction, 3D indoor environment map, multiple ICP algorithm.ru
dc.titleAccurate reconstruction of the 3D indoor environment map with a RGB-D camera based on multiple ICPru
dc.typeArticleru
dc.textpartIV Международная конференция и молодёжная школа «Информационные технологии и нанотехнологии» (ИТНТ-2018) 1288 Figure 1. Pair of frames. Figure 2. Point cloud from the first frame. Note that by applying the ICP algorithm to sub-clouds a set of transformation matrices are obtained. The transformation matrices could be used in different ways for reconstruction of the 3D dense map of the environment: each matrix tforms_i from ICP algorithm to each i sub-cloud, or tform_1 for all sub-clouds. ...-
Располагается в коллекциях: Информационные технологии и нанотехнологии

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