When registering point clouds resolved from an underlying 2-D pixel structure, such as those resulting from structured light and flash LiDAR sensors, or stereo reconstruction, it is expected that some points in one cloud do not have corresponding points in the other cloud, and that these would occur together, such as along an edge of the depth map. In this work, a hidden Markov random field model is used to capture this prior within the framework of the iterative closest point algorithm. The EM algorithm is used to estimate the distribution parameters and the hidden component memberships. Experiments are presented demonstrating that this method outperforms several other outlier rejection methods when the point clouds have low or moderate overlap.
Several attempts have been made to improve registration performance in the low-overlap regime. The hybrid genetic hill-climbing algorithm of @cite_5 @cite_32 shows success with overlaps down to 55 a direction angle'' on points and then aligns clouds in rotation using a histogram of these, and in translation using correlations of 2-D projections. Rotational alignment is recovered using extended Gaussian images in @cite_46 , and refined with ICP, showing success with overlap as low as 45 Use of robust statistics or error metrics besides sum of squared point-to-point distance has made ICP more robust to outliers. The point-to-plane @cite_29 metric, where the distance is measured as it is projected onto the surfance normal of the fixed point, is frequently used and has been shown to be more robust in the case of limited overlap @cite_8 . Color information can also be exploited @cite_2 @cite_30 . Sparse norms are used within ICP in @cite_23 , which can be sped up using simulated annealing @cite_26 .
@cite_30 @cite_26 @cite_8 @cite_29 @cite_32 @cite_23 @cite_2 @cite_5 @cite_46
2159029220 2118631462 2085100338 2165050169 2157364958
This paper presents methodologies to accelerate the registration of 3D point cloud segments by using hue data from the associated imagery. The proposed variant of the Iterative Closest Point (ICP) algorithm combines both normalized point range data and weighted hue value calculated from RGB data of an image registered 3D point cloud. A k-d tree based nearest neighbor search is used to associated common points in x, y, z, hue 4D space. The unknown rigid translation and rotation matrix required for registration is iteratively solved using Singular Value Decomposition (SVD) method. A mobile robot mounted scanner was used to generate color point cloud segments over a large area. The 4D ICP registration has been compared with typical 3D ICP and numerical results on the generated map segments shows that the 4D method resolves ambiguity in registration and converges faster than the 3D ICP The three-dimensional reconstruction of real objects is an important topic in computer vision. Most of the acquisition systems are limited to reconstruct a partial view of the object obtaining in blind areas and occlusions, while in most applications a full reconstruction is required. Many authors have proposed techniques to fuse 3D surfaces by determining the motion between the different views. The first problem is related to obtaining a rough registration when such motion is not available. The second one is focused on obtaining a fine registration from an initial approximation. In this paper, a survey of the most common techniques is presented. Furthermore, a sample of the techniques has been programmed and experimental results are reported to determine the best method in the presence of noise and outliers, providing a useful guide for an interested reader including a Matlab toolbox available at the webpage of the authors. In general, multiple views are required to create a complete 3D model of an object or of a multi-roomed indoor scene. In this work, we address the problem of merging multiple textured 3D data sets, each of which corresponds to a different view of a scene. There are two steps to the merging process: registration and integration. To register, or align, data sets we use a modified version of the iterative closest point (ICP) algorithm; our version, which we call color ICP, considers not only 3D information, but color as well. We show that the use of color decreases registration error significantly when using omnidirectional stereo data sets. Once the 3D data sets have been registered, we integrate them to produce a seamless, composite 3D textured model. Our approach to integration uses a 3D occupancy grid to represent likelihood of spatial occupancy through voting. In addition to occupancy information, we store surface normal in each voxel of the occupancy grid. Surface normal is used to robustly extract a surface from the occupancy grid; on that surface we blend textures from multiple views. In digital Archaeology, the 3D modeling of physical objects from range views is an important issue. Generally, the applications demand a great number of views to create a precise 3D model through a registration process. Most range image registration techniques are based on variants of the ICP (Iterative Closest Point) algorithm. The ICP algorithm has two main drawbacks: the possibility of convergence to a local minimum, and the need to prealign the images. Genetic Algorithms (GAs) were recently applied to range image registration providing good convergence results without the constraints observed in the ICP approaches. To improve range image registration, we explore the use of GAs and develop a novel approach that combines a GA with hillclimbing heuristics (GH). The experimental results show that our method is effective in aligning low overlap views and yield more accurate registration results than either ICP or standard GA approaches. Our method is highly advantageous in archaeological applications, where it is necessary to reduce the number of views to be aligned because data acquisition is expensive and also to minimize error accumulation in the 3D model. We also present a new measure of surface interpenetration with which to evaluate the registration and prove its utility with experimental results. We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two point sets can have very little overlap. Crude alignment is achieved by estimation of the 3D-rotation from two Extended Gaussian Images even when the data sets inducing them have partial overlap. The technique is based on the correlation of the two EGIs in the Fourier domain and makes use of the spherical and rotational harmonic transforms. For pairs with low overlap which fail a critical verification step, the rotational alignment can be obtained by the alignment of constellation images generated from the EGIs. Rotationally aligned sets are matched by correlation using the Fourier transform of volumetric functions. A fine alignment is acquired in the final step by running Iterative Closest Points with just few iterations.