This paper presents a mutual information (MI) based algorithm for the estimation of full 6-degree-of-freedom (DOF) rigid body transformation between two overlapping point clouds. We first divide the scene into a 3D voxel grid and define simple to compute features for each voxel in the scan. The two scans that need to be aligned are considered as a collection of these features and the MI between these voxelized features is maximized to obtain the correct alignment of scans. We have implemented our method with various simple point cloud features (such as number of points in voxel, variance of z-height in voxel) and compared the performance of the proposed method with existing point-to-point and point-to-distribution registration methods. We show that our approach has an efficient and fast parallel implementation on GPU, and evaluate the robustness and speed of the proposed algorithm on two real-world datasets which have variety of dynamic scenes from different environments.
Iterative Closest Point (ICP) @cite_26 is one of the most popular scan registration algorithms used to estimate the optimal transformation between two overlapping scans. In ICP, closest points between scan @math and reference scan @math are used to obtain a closed-form solution by optimizing the sum of squared distances (usually Euclidean distance). Performing the nearest neighbor search in ICP becomes a bottleneck due to its high computational cost, however, using a K-D Tree @cite_21 does mitigate the problem to an extent. Iterative Dual Correspondence (IDC) @cite_9 generates corresponding points for both rotation and translation separately, with optimization done in an alternate fashion. This improves the alignment accuracy when the initial estimate has large rotational error. Iterative Closest Line (ICL) @cite_14 , @cite_12 , @cite_2 is a variant of ICP in which instead of matching the points in both the scans, the query points in scan @math are matched to lines extracted from points in reference scan @math . Generalized ICP (GICP) @cite_1 , attaches a probabilistic model in estimating the correct corresponding points by taking the covariance structure derived from the local neighborhood in the environment.
@cite_14 @cite_26 @cite_9 @cite_21 @cite_1 @cite_2 @cite_12
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Point set registration is important for calibration of multiple cameras, 3D reconstruction and recognition, etc. The iterative closest point (ICP) algorithm is accurate and fast for point set registration in a same scale, but it does not handle the case with different scales. This paper instead introduces a novel approach named the scaling iterative closest point (SICP) algorithm which integrates a scale matrix with boundaries into the original ICP algorithm for scaling registration. At each iterative step of this algorithm, we set up correspondence between two m-D point sets, and then use a simple and fast iterative algorithm with the singular value decomposition (SVD) method and the properties of parabola incorporated to compute scale, rotation and translation transformations. The SICP algorithm has been proved to converge monotonically to a local minimum from any given parameters. Hence, to reach desired global minimum, good initial parameters are required which are successfully estimated in this paper by analyzing covariance matrices of point sets. The SICP algorithm is independent of shape representation and feature extraction, and thereby it is general for scaling registration of m-D point sets. Experimental results demonstrate its efficiency and accuracy compared with the standard ICP algorithm. 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. > 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 Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the @math error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space @math . By exploiting the special structure of @math geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.