Global Hybrid Registration for 3D Constructed Surfaces using Ray-casting and Improved Self Adaptive Differential Evolution Algorithm (original) (raw)
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2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013
Range image registration is one of the fundamental tasks in 3D computer vision and robotics which is gaining more attention with availability of affordable range cameras. Existing recent research has considered application or extension of well known point features like SIFT to the range data; examples include Shape index SIFT and 2.5D SIFT. Compared to RGB image, the quality of range measurement is much worse in sensors like Kinect. This is expected and inherent due to the exploited structured light technique. Therefore, point features may easily mismatched as a result of higher noise level. In this paper we show how using region based features may overcome this challenge. MSER features are extracted from shape index image obtained from the input range image. A SIFT-like descriptor is then proposed to encode major smooth regions of the scene as stable features invariant to scale, rotation and affine transformations. Experimental results are obtained using range image databases of Ohio State University and Stuttgart University which show improvement on the percentage of correct matched features and stability of detected features.
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Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight camera, is a challenging task with important applications in robotics and computer vision. Due to the recent advent of cheap depth sensing devices, many different 3D registration algorithms have been proposed in literature, focussing on different domains such as localization and mapping or image registration. In this survey paper, we review the state-of-the-art registration algorithms and discuss their common mathematical foundation. Starting from simple deterministic methods, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), more recently introduced approaches such as Iterative Closest Point (ICP) and its variants, are analyzed and compared. The main contribution of this paper therefore consists of an overview of registration algorithms that are of interest in the field of computer vision and robotics, for example Simultaneous Localization and Mapping.
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3D point cloud data is used for 3D model acquisition, geometry processing and 3D inspection. Registration of 3D point cloud data is crucial for each field. The difference between 2D image registration and 3D point cloud registration is that the latter requires several things to be considered: translation on each plane, rotation, tilt and etc. This paper describes a method of registering 3D point cloud data with noise. The relationship between the two sets of 3D point cloud data can be obtained by Affine transformation. In order to calculate 3D Affine transformation matrix, corresponding points are required. To find the corresponding points, we use the height map which is projected from 3D point cloud data onto XY plane. We formulate the height map matching as a cost function and estimate the corresponding points. To find the proper 3D Affine transformation matrix, we formulate a cost function which uses the relationship of the corresponding points. Also the proper 3D Affine transformation matrix can be calculated by minimizing the cost function. The experimental results show that the proposed method can be applied to various objects and gives better performance than the previous work.