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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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. The iterative closest point (ICP) algorithm [2] is a popular method for modeling 3D objects from range data. The classical ICP algorithm rests on a rigid surface assumption. Building on recent work on nonrigid object models [5; 16; 9], this paper presents an ICP algorithm capable of modeling nonrigid objects, where individual scans may be subject to local deformations. We describe an integrated mathematical framework for simultaneously registering scans and recovering the surface configuration. To tackle the resulting high-dimensional optimization problems, we introduce a hierarchical method that first matches a coarse skeleton of scan points, then adapts local scan patches. The approach is implemented for a mobile robot capable of acquiring 3D models of objects. This paper describes PLICP, an ICP (iterative closest corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download. 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. 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. 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. Most current navigation algorithms in mobile robotics produce 2D maps from data provided by 2D sensors. In large part this is due to the availability of suitable 3D sensors and difficulties of managing the large amount of data supplied by 3D sensors. This paper presents a novel, multi-resolution algorithm that aligns 3D range data stored in occupied voxel lists so as to facilitate the construction of 3D maps. Multi-resolution occupied voxel lists (MROL) are voxel based data structures that efficiently represent 3D scan and map information. The process described in this research can align a sequence of scans to produce maps and localise a range sensor within a prior global map. An office environment (200 square metres) is mapped in 3D at 0.02 m resolution, resulting in a 200,000 voxel occupied voxel list. Global localisation within this map, with no prior pose estimate, is completed in 5 seconds on a 2 GHz processor. The MROL based sequential scan matching is compared to a standard iterative closest point (ICP) implementation with an error in the initial pose estimate of plus or minus 1 m and 90 degrees. MROL correctly scan matches 94 of scans to within 0.1 m as opposed to ICP with 30 within 0.1 m. In this paper we investigate the usage of persistent point feature histograms for the problem of aligning point cloud data views into a consistent global model. Given a collection of noisy point clouds, our algorithm estimates a set of robust 16D features which describe the geometry of each point locally. By analyzing the persistence of the features at different scales, we extract an optimal set which best characterizes a given point cloud. The resulted persistent features are used in an initial alignment algorithm to estimate a rigid transformation that approximately registers the input datasets. The algorithm provides good starting points for iterative registration algorithms such as ICP (Iterative Closest Point), by transforming the datasets to its convergence basin. We show that our approach is invariant to pose and sampling density, and can cope well with noisy data coming from both indoor and outdoor laser scans. 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. Data acquisition in large-scale scenes regularly involves accumulating information across multiple scans. A common approach is to locally align scan pairs using Iterative Closest Point (ICP) algorithm (or its variants), but requires static scenes and small motion between scan pairs. This prevents accumulating data across multiple scan sessions and or different acquisition modalities (e.g., stereo, depth scans). Alternatively, one can use a global registration algorithm allowing scans to be in arbitrary initial poses. The state-of-the-art global registration algorithm, 4PCS, however has a quadratic time complexity in the number of data points. This vastly limits its applicability to acquisition of large environments. We present Super 4PCS for global pointcloud registration that is optimal, i.e., runs in linear time (in the number of data points) and is also output sensitive in the complexity of the alignment problem based on the (unknown) overlap across scan pairs. Technically, we map the algorithm as an 'instance problem' and solve it efficiently using a smart indexing data organization. The algorithm is simple, memory-efficient, and fast. We demonstrate that Super 4PCS results in significant speedup over alternative approaches and allows unstructured efficient acquisition of scenes at scales previously not possible. Complete source code and datasets are available for research use at http: geometry.cs.ucl.ac.uk projects 2014 super4PCS . 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 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 We present a real-time SLAM system that combines an improved version of the Iterative Closest Point (ICP) and inertial dead reckoning to localize our dynamic quadrupedal machine in a local map. Despite the strong and fast motions induced by our 80 kg hydraulic legged robot, the SLAM system is robust enough to keep the position error below 5 within the local map that surrounds the robot. The 3D map of the terrain, computed at the camera frame rate is suitable for vision based planned locomotion. The inertial measurements are used before and after the ICP registration, to provide a good initial guess, to correct the output and to detect registration failures which can potentially corrupt the map. The performance in terms of time and accuracy are also doubled by preprocessing the point clouds with a background subtraction prior to performing the ICP alignment. Our local mapping approach, in spite of having a global frame of reference fixed onto the ground, aligns the current map to the body frame, and allows us to push the drift away from the most recent camera scan. The system has been tested on our robot by performing a trot around obstacles and validated against a motion capture system. This paper describes 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 experience shows that 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. For examples, a given 'model' shape and a sensed 'data' shape that represents a major portion of the model shape can be registered in minutes by testing one initial translation and a relatively small set of rotations to allow for the given level of model complexity. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection. The described method is also useful for deciding fundamental issues such as the congruence (shape equivalence) of different geometric representations as well as for estimating the motion between point sets where the correspondences are not known. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. We present a novel way of odometry estimation from Velodyne LiDAR point cloud scans. The aim of our work is to overcome the most painful issues of Velodyne data - the sparsity and the quantity of data points - in an efficient way, enabling more precise registration. Alignment of the point clouds which yields the final odometry is based on random sampling of the clouds using Collar Line Segments (CLS). The closest line segment pairs are identified in two sets of line segments obtained from two consequent Velodyne scans. From each pair of correspondences, a transformation aligning the matched line segments into a 3D plane is estimated. By this, significant planes (ground, walls, …) are preserved among aligned point clouds. Evaluation using the KITTI dataset shows that our method outperforms publicly available and commonly used state-of-the-art method GICP for point cloud registration in both accuracy and speed, especially in cases where the scene lacks significant landmarks or in typical urban elements. For such environments, the registration error of our method is reduced by 75 compared to the original GICP error. 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 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. > This letter presents a novel method for visual odometry estimation from a RGB-D camera. The camera motion is estimated by aligning a source to a target RGB-D frame using an intensity-assisted iterative closest point (ICP) algorithm. The proposed method differs from the conventional ICP in following aspects. 1) To reduce the computational cost, salient point selection is performed on the source frame, where only points that contain valuable information for registration are used. 2) To reduce the influence of outliers and noises, robust weighting function is proposed to weight corresponding pairs based on statistics of their spatial distances and intensity differences. 3) The obtained robust weighting function from 2) is used for correspondence estimation of the following ICP iteration. The proposed method runs in real-time with a single core CPU thread, hence it is suitable for robots with limited computation resources. The evaluation on TUM RGB-D benchmark shows that in the majority of the tested sequences, our proposed method outperforms state-of-the-art accuracy in terms of translational drift per second with a computation speed of 78 Hz. The digitization of the 3D shape of real objects is a rapidly expanding field, with applications in entertainment, design, and archaeology. We propose a new 3D model acquisition system that permits the user to rotate an object by hand and see a continuously-updated model as the object is scanned. This tight feedback loop allows the user to find and fill holes in the model in real time, and determine when the object has been completely covered. Our system is based on a 60 Hz. structured-light rangefinder, a real-time variant of ICP (iterative closest points) for alignment, and point-based merging and rendering algorithms. We demonstrate the ability of our prototype to scan objects faster and with greater ease than conventional model acquisition pipelines. We present a robust plane finding algorithm that when combined with plane-based frame-to-frame registration gives accurate real-time pose estimation. Our plane extraction is capable of handling large and sparse datasets such as those generated from spinning multi-laser sensors such as the Velodyne HDL-32E LiDAR. We test our algorithm on frame-to-frame registration in a closed-loop indoor path comprising 827 successive 3D laser scans (over 57 million points), using no additional information (e.g., odometry, IMU). Our algorithm outperforms, in both accuracy and time, three state-of-the-art methods, based on iterative closest point (ICP), plane-based randomized Hough transform, and planar region growing.