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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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. We present an algorithm for the automatic alignment of two 3D shapes (data and model), without any assumptions about their initial positions. The algorithm computes for each surface point a descriptor based on local geometry that is robust to noise. A small number of feature points are automatically picked from the data shape according to the uniqueness of the descriptor value at the point. For each feature point on the data, we use the descriptor values of the model to find potential corresponding points. We then develop a fast branch-and-bound algorithm based on distance matrix comparisons to select the optimal correspondence set and bring the two shapes into a coarse alignment. The result of our alignment algorithm is used as the initialization to ICP (iterative closest point) and its variants for fine registration of the data to the model. Our algorithm can be used for matching shapes that overlap only over parts of their extent, for building models from partial range scans, as well as for simple symmetry detection, and for matching shapes undergoing articulated motion. This paper reports a novel algorithm for bootstrapping the automatic registration of unstructured 3D point clouds collected using co-registered 3D lidar and omnidirectional camera imagery. Here, we exploit the co-registration of the 3D point cloud with the available camera imagery to associate high dimensional feature descriptors such as scale invariant feature transform (SIFT) or speeded up robust features (SURF) to the 3D points. We first establish putative point correspondence in the high dimensional feature space and then use these correspondences in a random sample consensus (RANSAC) framework to obtain an initial rigid body transformation that aligns the two scans. This initial transformation is then refined in a generalized iterative closest point (ICP) framework. The proposed method is completely data driven and does not require any initial guess on the transformation. We present results from a real world dataset collected by a vehicle equipped with a 3D laser scanner and an omnidirectional camera. 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.