Traditional ICP Algorithm Research Articles

Indoor Robot SLAM with Multi-Sensor Fusion

Abstract In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor fusion SLAM algorithm for indoor robots was proposed. Aiming at the mismatch problem of the traditional ICP algorithm in the front end of the lidar SLAM, the algorithm adopts the PL-ICP algorithm that is more suitable for the indoor environment, and uses the extended Kalman filter to fuse the wheel odometer and IMU to provide the initial motion estimation value. Then, during the mapping phase, the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to compensate for the lack of vertical field of view in the 2D lidar mapping. The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33% compared to a single wheeled odometer, providing a higher initial iteration value for the PL-ICP algorithm. At the same time, fusion mapping compensates for the shortcomings of a single two-dimensional lidar mapping, and constructs an environmental map with more complete environmental information.

Salient Preprocessing: Robotic ICP Pose Estimation Based on SIFT Features

The pose estimation can be effectively solved according to the feature point matching relationship in RGB-D. However, the extraction and matching process based on the whole image’s feature point is very computationally intensive and lacks robustness, which is the bottleneck of the traditional ICP algorithm. This paper proposes representing the whole image’s feature points by the salient objects’ robustness SIFT feature points through the salient preprocessing, and further solving the pose estimation. The steps are as follows: (1) salient preprocessing; (2) salient object’s SIFT feature extraction and matching; (3) RANSAC removes mismatching salient feature points; (4) ICP pose estimation. This paper proposes salient preprocessing aided by RANSAC processing based on the SIFT feature for pose estimation for the first time, which is a coarse-to-fine method. The experimental results show that our salient preprocessing algorithm can coarsely reduce the feature points’ extractable range and interfere. Furthermore, the results are processed by RANSAC good optimization, reducing the calculation amount in the feature points’ extraction process and improving the matching quality of the point pairs. Finally, the calculation amount of solving R, t based on all the matching feature points is reduced and provides a new idea for related research.

Point cloud registration method based on Particle Swarm Optimization algorithm and improved ICP

In order to achieve accurate wheel gripping by truss manipulators, accurate point cloud alignment of train wheels is required. In this paper, the wheels are detected by binocular laser displacement sensors. And accurate point cloud alignment is achieved by using the point cloud denoising and streamlining algorithm, particle swarm-based algorithm, and improved ICP algorithm for 3D point cloud data. The experimental results show that reducing noise and data volume can improve the stitching effect and alignment efficiency of 3D point cloud models. The algorithm of this paper is experimentally verified to be 43% better in error accuracy and 93% faster in time-consuming than the traditional ICP algorithm, which verifies the effectiveness of its method.

Transformer Connection Terminal Registration Based on Objective Function

In recent years, the traditional ICP algorithm has high requirements for the initial position of point clouds, and low registration ability for point clouds with low overlap. The Microsoft Kinect depth sensor was used to obtain the point cloud data of the target object from the real scene. Then, pre-processing such as point cloud segmentation, filtering and down sampling. In the coarse registration, the feature point sampling consistency algorithm was used to make the point cloud obtain a better initial position. Finally, a point-to-surface ICP algorithm optimized by linear least squares was proposed in the fine registration. The experimental results show that the root mean square error of the improved algorithm is 0.761mm and the time is 52.32ms. Compared with the ICP algorithm based on SIFT feature points and the improved ICP algorithm based on feature point sampling consistency, the registration accuracy of the improved algorithm is increased by 21.0% and 43.3%, and the speed is increased by 18.9% and 30.2%.

RGB-ICP Method to Calculate Ground Three-Dimensional Deformation Based on Point Cloud from Airborne LiDAR

With the rapid development of LiDAR technology in recent years, high-resolution LiDAR data possess a great capability to describe fine surface morphology in detail; thus, differencing multi-temporal datasets becomes a powerful tool to explain the surface deformation process. Compared with other differencing methods, ICP algorithms can directly estimate 3D displacements and rotations; thus, surface deformation parameters can be obtained by aligning window point clouds. However, the traditional ICP algorithm usually requires a good initial pose of the point cloud and relies on calculating the spatial distance to match the corresponding points, which can easily lead the algorithm to the local optimum. To address the above problems, we introduced the color information of the point cloud and proposed an improved ICP method that fuses RGB (RGB-ICP) to reduce the probability of matching errors by filtering color-associated point pairs, thus improving the alignment accuracy. Through simulated experiments, the ability of the two algorithms to estimate 3D deformation was compared, and the RGB-ICP algorithm could significantly reduce the deformation deviation (30–95%) in the three-dimensional direction. In addition, the RGB-ICP algorithm was applicable to different terrain structures, especially for smooth terrain, where the improvement was the most effective in the horizontal direction. Finally, it is worth believing that the RGB-ICP algorithm can play a unique role in surface change detection and provide a reliable basis for explaining the surface motion process.

A research of surgical navigation system of mixed reality based on image registration

This paper studies a registration method that is more suitable for using Microsoft HoloLens as a surgical navigation instrument. The most important step in the surgical navigation process is to register the virtual model with the corresponding lesion. Our registration method is mainly as follows: first use the 3D model recognition in vuforia for coarse registration; then extract the point cloud data of model and real scene scanned by HoloLens; finally use the normal vector geometric features to extract the feature points of the point cloud data, and then use the ICP algorithm Perform fine registration. Our registration method can solve the problem that the traditional ICP algorithm is easy to fall into a local optimum, and it can also improve the registration accuracy to a certain extent.

Fast and coarse registration of point cloud in ICP three-dimensional space

The traditional ICP algorithm has a large amount of computation in the process of searching for corresponding points[1].This is the bottleneck of the traditional ICP algorithm[2],and the standard ICP is looking for the corresponding point[3].The point where the Euclidean distance is closest is the corresponding point[4].Unreasonable,there will be a certain number of wrong points[5].In order to improve the accuracy and speed of 3D point cloud data registration,this paper combines the coarse registration of the center of the box and the circular domain search method of the rasterized small cube to further improve and optimize the traditional ICP algorithm[6].

Comparison of Several Different Registration Algorithms

Abstract The common point cloud registration algorithms are usually divided into initial registration and precise registration. In this paper, SAC-IA algorithm, which is commonly used in PCL, is selected for initial registration, and the traditional ICP algorithm is used for accurate registration. Three different feature descriptors (3D shape context, Point Feature Histograms, Fast Point Feature Histograms) are used to realize SAC-IA algorithm and ICP precise registration algorithm. During the implementation of the algorithm, the registration time and registration error of point cloud are calculated; according to the experimental results, the registration time and registration error of SAC-IA algorithm and ICP algorithm based on three different descriptors are compared. The results show that the registration algorithm based on 3D shape context has high accuracy, but the registration time is too long, which is not suitable for a large number of point cloud data; the registration algorithm based on fast point feature histograms has short registration time and good registration effect.

Point Cloud Registration Method for Pipeline Workpieces Based On NDT and Improved ICP Algorithms

A new point cloud registration method based on NDT and improved ICP algorithm is proposed to solve the problem of point cloud registration of laser scanning workpiece position and pose data on industrial pipeline. Firstly, according to the FPFH algorithm, the feature points of point clouds are extracted to reduce the number of point clouds. Then, the normal distribution transformation (NDT) is used to achieve rough registration of point clouds, so that the two point clouds have relatively good initial position and posture. Finally, based on the traditional ICP algorithm, the k-d tree is used to accelerate the search speed of the corresponding point pairs and complete the accurate registration of the point clouds. The proposed algorithm and the NDT+ICP algorithm are tested respectively, and the results show that the proposed algorithm is faster, with lower error and fewer iterations, which provides a reference for similar problems.

Point Cloud Registration Method for Pipeline Workpieces Based on 4PCS and Improved ICP Algorithms

A new point cloud registration method based on 4PCS and improved ICP algorithm is proposed to solve the problem of point cloud registration of laser scanning workpiece position and pose data on industrial pipeline. Firstly, feature points set of point cloud is extracted from normal vector. Then, the rough registration of point cloud is realized by using the registration algorithm based on feature points (4PCS), and the initial recognition of workpiece position and pose is completed. Finally, based on the traditional ICP algorithm, the k-d tree is used to accelerate the search speed of the corresponding point pairs and complete the accurate registration of the point clouds. The proposed algorithm and the 4PCS+ICP algorithm are tested respectively, and the results show that the algorithm is faster, with lower error and fewer iterations.

Application of Particle Swarm Optimization Algorithm in Geomagnetic Matching Navigation

In order to improve the positioning accuracy of the traditional geomagnetic matching algorithm, a new geomagnetic matching algorithm based on particle swarm optimization is proposed. The algorithm plans the search range of the real position centering on the measurement position of the reference navigation system. Extracting the corresponding geomagnetic data from the reference geomagnetic map, the particle swarm optimization algorithm is then introduced into the matching region search, and the obtained geomagnetic profile values are taken as particles in the particle population. On this basis, the normalized product correlation function is used as the particle fitness function, comparing the similarity between the reference sub graph and the real-time graph profile by the maximum fitness measure, Finally, through simulation analysis, the matching precision and matching time of matching algorithm based on ICP and PSO are compared. The simulation results show that the matching effect based on PSO algorithm is better than the traditional ICP algorithm. Although the matching time is slightly longer than the ICP algorithm, it meets the real-time requirements. Geomagnetic correlation matching algorithm is an extremely important type of algorithm in geomagnetic aided navigation algorithm. The current research focuses on the improvement of the traditional Terrain Contour Matching (TERCOM) algorithm metric function and the introduction of new metric functions and various ICP geomagnetic matching algorithms [1-4].The geomagnetic correlation matching algorithm of the aircraft not only requires high precision and high reliability, but also the execution efficiency of the algorithm is one of the important contents of the research. Based on the research of traditional geomagnetic correlation matching algorithm, this paper introduces particle swarm optimization algorithm into geomagnetic correlation matching algorithm. From the aspects of similarity measure function, geomagnetic matching feature quantity and search strategy, a new method of geomagnetic correlation matching for aircraft is proposed.

An ICP algorithm based on block path closest point search

At present, the traditional ICP algorithm has the problems of low efficiency and low precision. To solve these two problems, an ICP algorithm based on block path closest point search is proposed in this paper. The idea of the algorithm is as follows: firstly, the point cloud data is divided into blocks, and the nearest point block corresponding to the target point cloud is searched by the path method. Secondly, according to the global method, the nearest point can be determined only by finding the nearest point block, and complete all the closest match. The experimental results show that the improved ICP algorithm has faster speed and higher precision than the traditional ICP algorithm, for a large number of point cloud data advantage is more obvious.

An Improved Iterative Closest Points Algorithm

Visual method including binocular stereo vision method and monocular vision method of the relative position and pose measurement for space target has become relatively mature, and many researchers focus on the method based on three-dimension measurement recently. ICP alignment, which is the key of three-dimension pattern measurement method, has the problem of low efficiency in large data sets. Considering this problem, an improved ICP algorithm is proposed in this paper. The improved ICP algorithm is the combination of the original ICP algorithm and KD-TREE. The experimental comparison between the improved ICP algorithm and the traditional ICP algorithm in efficiency has been given in this paper, which shows that the improved ICP algorithm can get much better performance.

Automatic registration of overlapping 3D point clouds using closest points

While the SoftAssign algorithm imposes a two-way constraint embedded into the deterministic annealing scheme and the EMICP algorithm imposes a one-way constraint, they represent the state of the art technique for the automatic registration of overlapping free form shapes. They both have a time complexity of O( n 2). While the former has a space complexity also of O( n 2), the latter has a space complexity of O( n). The heavy demand for computation and storage memory renders either the SoftAssign or EMICP algorithm to hardly operate on whole shapes with thousands of points. In this case, they often have to reduce the number of points to an order of 100s on the free form shapes to be registered. This paper proposes using closest points in conjunction with either the one-way or two-way constraint for the automatic registration of overlapping 3D point clouds and thus, combining the accuracy of both the SoftAssign and EMICP algorithms with the efficiency of the traditional ICP algorithm. A comparative study based on both synthetic data and real images has shown that the proposed algorithm does not significantly sacrifice accuracy and stability of either the SoftAssign or EMICP algorithm, but gains remarkable efficiency of the traditional ICP algorithm for the automatic registration of overlapping 3D point clouds. Since, the proposed algorithm is of general use and has an advantage of easy implementation, it is likely to become in the future a benchmark for the automatic registration of overlapping 3D point clouds.

Projecting registration error for accurate registration of overlapping range images

In this paper, we propose a novel algorithm for the automatic registration of two overlapping range images. Since it is relatively difficult to compare the registration errors of different point matches, we project them onto a virtual image plane for more accurate comparison using the classical pin-hole perspective projection camera model. While the traditional ICP algorithm is more interested in the points in the second image close to the sphere centred at the transformed point, the novel algorithm is more interested in the points in the second image as collinear as possible to the transformed point. The novel algorithm then extracts useful information from both the registration error and projected error histograms for the elimination of false matches without any feature extraction, image segmentation or the requirement of motion estimation from outliers corrupted data and, thus, has an advantage of easy implementation. A comparative study based on real images captured under typical imaging conditions has shown that the novel algorithm produces good registration results.