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.
Since ICP requires good initialization, a significant body of work has been devoted to achieving global registration or finding an approximate alignment from any initial state, which is then refined with ICP. @ @cite_6 @cite_44 , an initial alignment is found by matching sets of 4 coplanar points, using ratios invariant to match sets; good performance with low overlap is claimed. Fast global registration between two or more point clouds is achieved in @cite_35 by finding correspondences once based on point features, and then using robust estimators to ignore spurious correspondences. Kernel correlation @cite_25 registers point clouds by minimizing the entropy of the resulting joint cloud. @cite_10 minimizes the Hausdorff distance of several descriptors using particle swarm optimization to find the globally optimal alignment. LM-ICP @cite_19 uses Levenberg-Marquardt to directly minimize the registration error over transformations and closest-point distances; derivatives for closest-point distances are precomputed with finite differences of a discretized distance transform.
@cite_35 @cite_6 @cite_44 @cite_19 @cite_10 @cite_25
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Despite the fact that original Iterative Closest Point(ICP) algorithm has been widely used for registration, itcannot tackle the problem when two point clouds are par-tially overlapping. Accordingly, this paper proposes a ro-bust approach for the registration of partially overlappingpoint clouds. Given two initially posed clouds, it firstlybuilds up bilateral correspondence and computes bidirec-tional distances for each point in the data shape. Based onthe ratio of bidirectional distances, the exponential functionis selected and utilized to calculate the probability value,which can indicate whether the point pair belongs to theoverlapping part or not. Subsequently, the probability val-ue can be embedded into the least square function for reg-istration of partially overlapping point clouds and a novelvariant of ICP algorithm is presented to obtain the optimalrigid transformation. The proposed approach can achievegood registration of point clouds, even when their overlappercentage is low. Experimental results tested on public da-ta sets illustrate its superiority over previous approaches onrobustness. In this paper, we present a robust global approach for point cloud registration from uniformly sampled points. Based on eigenvalues and normals computed from multiple scales, we design fast descriptors to extract local structures of these points. The eigenvalue-based descriptor is effective at finding seed matches with low precision using nearest neighbor search. Generally, recovering the transformation from matches with low precision is rather challenging. Therefore, we introduce a mechanism named correspondence propagation to aggregate each seed match into a set of numerous matches. With these sets of matches, multiple transformations between point clouds are computed. A quality function formulated from distance errors is used to identify the best transformation and fulfill a coarse alignment of the point clouds. Finally, we refine the alignment result with the trimmed iterative closest point algorithm. The proposed approach can be applied to register point clouds with significant or limited overlaps and small or large transformations. More encouragingly, it is rather efficient and very robust to noise. A comparison to traditional descriptor-based methods and other global algorithms demonstrates the fine performance of the proposed approach. We also show its promising application in large-scale reconstruction with the scans of two real scenes. In addition, the proposed approach can be used to register low-resolution point clouds captured by Kinect as well. In this work, we analyze three different registration algorithms: Chamfer distance matching, the well-known iterated closest points (ICP) and an optic flow based registration. Their pairwise combination is investigated in the context of silhouette based pose estimation. It turns out that Chamfer matching and ICP used in combination do not only perform fairly well with small offset, but also deal with large offset significantly better than the other combinations. We show that by applying different optimized search strategies, the computational cost can be reduced by a factor eight. We further demonstrate the robustness of our method against simultaneous translation and rotation. The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >