Topographic mapping in planetary environments relies on accurate 3D scan registration methods. However, most global registration algorithms relying on features such as FPFH and Harris-3D show poor alignment accuracy in these settings due to the poor structure of the Mars-like terrain and variable resolution, occluded, sparse range data that is hard to register without some a-priori knowledge of the environment. In this paper, we propose an alternative approach to 3D scan registration using the curvelet transform that performs multi-resolution geometric analysis to obtain a set of coefficients indexed by scale (coarsest to finest), angle and spatial position. Features are detected in the curvelet domain to take advantage of the directional selectivity of the transform. A descriptor is computed for each feature by calculating the 3D spatial histogram of the image gradients, and nearest neighbor based matching is used to calculate the feature correspondences. Correspondence rejection using Random Sample Consensus identifies inliers, and a locally optimal Singular Value Decomposition-based estimation of the rigid-body transformation aligns the laser scans given the re-projected correspondences in the metric space. Experimental results on a publicly available data-set of planetary analogue indoor facility, as well as simulated and real-world scans from Neptec Design Group's IVIGMS 3D laser rangefinder at the outdoor CSA Mars yard demonstrates improved performance over existing methods in the challenging sparse Mars-like terrain.
One of the most popular scan registration methods, the iterative closest point (ICP) @cite_28 @cite_9 @cite_42 , relies on point-to-point correspondences to estimate the relative transformation of scans by minimizing the Euclidean distance error metric. The original ICP algorithm assumes that there exists a correspondence between each point of the source and model data-sets. This assumption is often violated with partially overlapping scans. Some modifications to the ICP algorithm have included the maximum error cutoff metric @cite_25 to account for false correspondences, and did not require every point to be matched. One of the key problems with scan registration is that the sparsely sampled corresponding points in two different scans often do not correspond to the same point in the 3D environment, but ICP assumes that they do. In addition, the quality of the ICP solution depends heavily on the availability of good initial estimates of the transformation @cite_28 .
@cite_28 @cite_9 @cite_42 @cite_25
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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. > Most range data registration techniques are variants on the iterative closest point (ICP) algorithm, proposed by Y. Chen and G. Medioni (1991, Proceedings of the IEEE Conference on Robotics and Automation) and P. J. Besl and N. D. McKay ( 1992, IEEE Trans. Pattern Anal. Mach. Intell. 14, 239-256). That algorithm, though, is only one approach to optimizing a least-squares point correspondence sum proposed by K. S. Arun. T. Huang, and S. D. Blostein (1987, IEEE Trans. Pattern Anal. Mach. Intell, 9, 698-700). In its basic form ICP has many problems, for example, its reliance on preregistration by hand close to the global minimum and its tendency to converge to suboptimal or incorrect solutions. This paper reports on an evolutionary registration algorithm which does not require initial prealignment and has a very broad basin of convergence. It searches many areas of a registration parameter space in parallel and has available to it a selection of evolutionary techniques to avoid local minima which plague both ICP and its variants. This paper presents a method for pairwise 3D alignment which solves data association by matching scan segments across scans. Generating accurate segment associations allows to run a modified version of the Iterative Closest Point (ICP) algorithm where the search for point-to-point correspondences is constrained to associated segments. The novelty of the proposed approach is in the segment matching process which takes into account the proximity of segments, their shape, and the consistency of their relative locations in each scan. Scan segmentation is here assumed to be given (recent studies provide various alternatives [10], [19]). The method is tested on seven sequences of Velodyne scans acquired in urban environments. Unlike various other standard versions of ICP, which fail to recover correct alignment when the displacement between scans increases, the proposed method is shown to be robust to displacements of several meters. In addition, it is shown to lead to savings in computational times which are potentially critical in real-time applications.