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Markerless Augmented Reality (AR) registration using the standard Homography matrix is unstable, and for image-based registration it has very low accuracy. In this paper, we present a new method to improve the stability and the accuracy of marker-less registration in AR. Based on the Visual Simultaneous Localization and Mapping (V-SLAM) framework, our method adds a three-dimensional dense cloud processing...
We introduce a new covariance descriptor combining object visual (color, gradient, depth, etc.) and geometric information (3D coordinates, normal vectors, Gaussian curvature, etc.) for mobile robot with RGB-D camera to deal with point cloud data. The improved mismatching correction algorithm is applied in the feature point mismatching correction of 3D point cloud, and then this correction algorithm...
Scene understanding is a critical issue in the advances of intelligent space. Given a 3D point cloud captured for a static scene by laser scanning, we propose a structure-based scene representation and object recognition method. Our method consists of three steps: (1) the scene is segmented and all of the segmented objects in scanned scene are described respectively by features, including size, location,...
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