Iterative closest point registration for fast point feature histogram features of a volume density optimization algorithm

In response to the issues of slow convergence and the tendency to fall into local optima in traditional iterative closest point (ICP) point cloud registration algorithms, this study presents a fast registration algorithm for laser point clouds based on 3D scale-invariant feature transform (3D-SIFT) feature extraction. First, feature points are preliminarily extracted using a normal vector threshold; then, more high-quality feature points are extracted using the 3D-SIFT algorithm, effectively reducing the number of point cloud registrations. Based on the extracted feature points, a coarse registration of the point cloud is performed using the fast point feature histogram (FPFH) descriptor combined with the sample consensus initial alignment (SAC-IA) algorithm, followed by fine registration using the point-to-plane ICP algorithm with a symmetric target function. The experimental results show that this algorithm significantly improved the registration efficiency. Compared with the traditional SAC-IA+ICP algorithm, the registration accuracy of this algorithm increased by 29.55% in experiments on a public dataset, and the registration time was reduced by 81.01%. In experiments on actual collected data, the registration accuracy increased by 41.72%, and the registration time was reduced by 67.65%. The algorithm presented in this paper maintains a high registration accuracy while greatly reducing the registration speed.

<title>Abstract</title> Wheels are critical components of railway vehicles, and the dynamic measurement of wheel parameters is of paramount importance for the safe operation of trains.To enhance the matching accuracy in the existing dynamic measurement processes for train wheel parameters, this paper proposes an improved point cloud registration algorithm based on key point fusion of the Super-4PCS(Super Four-Points Congruent Sets) and ICP (Iterative Closest Point) algorithm. Firstly, point cloud filtering and normal estimation are performed on the wheel point cloud data to obtain source and target point clouds with normal information. Subsequently, the ISS algorithm is employed to extract key points, and the FPFH(Fast Point Feature Histograms) point cloud feature descriptor is utilized to characterize the extracted key points. Then, a two-level registration strategy is used to improve registration accuracy, in which the Super-4PCS algorithm is applied for primary coarse registration and the ICP algorithm is used for the secondary fine registration, respectively. Finally, the experiment is conducted to validate the proposed algorithm and the performance of the algorithm is further comparative analyzed through the listed registration evaluation metrics. Experimental results demonstrate that the proposed algorithm exhibits significant improvements in registration accuracy and robustness for wheel point cloud data compared to the traditional algorithms.

Using Fast Point Feature Histograms feature from point cloud for 3D object recognition or registration, Fast Point Feature Histograms feature descriptor is arbitrarily and inefficiently calculated by subjectively adjusting the neighborhood radius, and the whole process can’t be completed automatically. An adaptive neighborhood-selection Fast Point Feature Histograms point cloud feature extraction algorithm has proposed to solve this problem. Firstly, we estimate the point cloud density of many pairs of point clouds. Secondly, compute the neighborhood radius to extract the Fast Point Feature Histograms features for Sample Consensus Initial Alignment registration, and count the radius and the density when the registration performance is optimal, and then the cubic spline interpolation fitting is used to obtain the function expression of the radius and the density. Finally, the Fast Point Feature Histograms feature extraction algorithm has combined with the function to form adaptive neighborhood-selection Fast Point Feature Histograms feature extraction algorithm. The experimental results have shown that the proposed algorithm can adaptively choose the appropriate neighborhood radius according to the density of point cloud, and improve the Fast Point Feature Histograms feature matching performance. At the same time, it is improved the computing speed to a better value, which is of guiding significance.

Point cloud segmentation is a key part of geometric processing. In this paper, a point cloud segmentation method based on the Fast Point Feature Histograms (FPFH) is proposed. Fast Point Feature Histograms (FPFH) is used to extract features of point clouds, and then Gaussian Mixture Model (GMM) is employed to cluster the point clouds. Experimental results on SHREC2014 dataset show the effectiveness of the proposed method. It can avoid both under- segmentation and over-segmentation.

The knee joint is a very disease-prone joint because it is deeply involved in the extension mechanism. In the case of patella dislocation, the rate of ligament damage after dislocation is high, and the possibility of serious injury is high. Currently, the diagnosis and analysis method of patella dislocation are performed using diagnostic imaging or palpation. However, these methods have some disadvantages. In diagnostic imaging, it takes a long time to diagnose and analyze, and the burden of the patient is heavy. In palpation, doctors tend to be subjective about diagnosis results and time-series recovery due to the loss of quantitative parameters. Therefore, in this study, the goal is to develop a system that can easily diagnose and analyze patella dislocation from the shape using a depth camera. As preparatory step of this, we develop a system that to detect the shape of the patella during knee flexion in healthy subjects and analyze the amount of move. Using the point cloud acquired from the depth camera, the patella was detected mainly by 3D registration using Fast Point Feature Histograms (FPFH) features, Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP). In experiment, three subjects were used to detect the patella during knee extension and analyze the amount of movement and posture, and a model was used to extract the point cloud of the patella shape needed for 3D registration from one subject. As a result, in the subject who is same subject of the patella model, we can get highly accurate detection and analysis results, and in the other two subjects, the detection accuracy of the patella was high. As an overall trend, we found that the patella moved in external rotation direction during knee flexion even in healthy subjects. As future works, it is necessary to investigate the detection accuracy of the patella and the tendency of analysis results when using this system for patients with symptoms of patella dislocation.

Three-dimensional (3D) reconstruction is an essential technique to visualize and monitor the growth of agricultural and forestry plants. However, inspecting tall plants (trees) remains a challenging task for single-camera systems. A combination of low-altitude remote sensing (an unmanned aerial vehicle) and a terrestrial capture platform (a mobile robot) is suggested to obtain the overall structural features of trees including the trunk and crown. To address the registration problem of the point clouds from different sensors, a registration method based on a fast point feature histogram (FPFH) is proposed to align the tree point clouds captured by terrestrial and airborne sensors. Normal vectors are extracted to define a Darboux coordinate frame whereby FPFH is calculated. The initial correspondences of point cloud pairs are calculated according to the Bhattacharyya distance. Reliable matching point pairs are then selected via random sample consensus. Finally, the 3D transformation is solved by singular value decomposition. For verification, experiments are conducted with real-world data. In the registration experiment on noisy and partial data, the root-mean-square error of the proposed method is 0.35% and 1.18% of SAC-IA and SAC-IA + ICP, respectively. The proposed method is useful for the extraction, monitoring, and analysis of plant phenotypes.

Three-dimensional point clouds captured by sensors can quantify plant phenotype, which plays an important role in agricultural intelligence. Many scanned objects in agriculture and forestry are tall and obscured by leaves, so point clouds captured by either terrestrial or airborne methods may be incomplete. In order to obtain a more complete point cloud, this paper proposes a point cloud registration method based on the fast point feature histogram (FPFH), which aligns point clouds collected from different viewpoints. This method calculates the FPFH feature of each point and the Bhattacharyya distance between point pairs. Effective strategies pick out reliable sets of point pairs. Singular value decomposition is used to obtain the transformation relationship between point clouds. Experimental results show that the proposed method has high accuracy for plant point cloud registration in real scenes, and the root-mean-square error is smaller than that of common registration methods of SAC-IA and NDT.KeywordsFast point feature histogramBhattacharyya distanceRandom sample consensusPoint cloud registration

Humans can interact with several kinds of machine (motor vehicle, robots, among others) in different ways. One way is through his/her head pose. In this work, we propose a head pose estimation framework that combines 2D and 3D cues using the concept of key frames (KFs). KFs are a set of frames learned automatically offline that consist the following: 2D features, encoded through Speeded Up Robust Feature (SURF) descriptors; 3D information, captured by Fast Point Feature Histogram (FPFH) descriptors; and target’s head orientation (pose) in real-world coordinates, which is represented through a 3D facial model. Then, the KF information is re-enforced through a global optimization process that minimizes error in a way similar to bundle adjustment. The KF allows to formulate, in an online process, a hypothesis of the head pose in new images that is then refined through an optimization process, performed by the iterative closest point (ICP) algorithm. This KF-based framework can handle partial occlusions and extreme rotations even with noisy depth data, improving the accuracy of pose estimation and detection rate. We evaluate the proposal using two public benchmarks in the state of the art: (1) BIWI Kinect Head Pose Database and (2) ICT 3D HeadPose Database. In addition, we evaluate this framework with a small but challenging dataset of our own authorship where the targets perform more complex behaviors than those in the aforementioned public datasets. We show how our approach outperforms relevant state-of-the-art proposals on all these datasets.

The body dimension measurement of large animals plays a significant role in quality improvement and genetic breeding, and the non-contact measurements by computer vision-based remote sensing could represent great progress in the case of dangerous stress responses and time-costing manual measurements. This paper presents a novel approach for three-dimensional digital modeling of live adult Qinchuan cattle for body size measurement. On the basis of capturing the original point data series of live cattle by a Light Detection and Ranging (LiDAR) sensor, the conditional, statistical outliers and voxel grid filtering methods are fused to cancel the background and outliers. After the segmentation of K-means clustering extraction and the RANdom SAmple Consensus (RANSAC) algorithm, the Fast Point Feature Histogram (FPFH) is put forward to get the cattle data automatically. The cattle surface is reconstructed to get the 3D cattle model using fast Iterative Closest Point (ICP) matching with Bi-directional Random K-D Trees and a Greedy Projection Triangulation (GPT) reconstruction method by which the feature points of cattle silhouettes could be clicked and calculated. Finally, the five body parameters (withers height, chest depth, back height, body length, and waist height) are measured in the field and verified within an accuracy of 2 mm and an error close to 2%. The experimental results show that this approach could be considered as a new feasible method towards the non-contact body measurement for large physique livestock.

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

The iterative closest point (ICP) algorithm is widely used in three-dimensional (3D) point cloud registration, and it is very stable and robust. However, its biggest drawback is being easily trapped in a local optimal solution, which results in the incorrect registration result. Currently, there is neither a clear effective range to define whether the ICP algorithm will fall into a local optimum nor a study providing a comprehensive evaluation of the ICP algorithm. In this paper, we take the overlap ratio, angle, distance, and noise as the influencing factors of ICP and evaluate the validity, robustness, accuracy, and efficiency of point-to-point and point-to-plane ICP by using four datasets. We first analyze the effective ranges of the two ICP algorithms with respect to overlap ratio, angle, and distance and then propose a universal effective range for the three factors. Next, the effect of Gaussian noise on the validity and accuracy of two ICPs is evaluated. We also analyze the factors influencing ICP accuracy and explain their changing rules. We finally study the effect of different parameters on the efficiency. All results are compared by using point-to-point and point-to-plane ICP algorithms. The results show that the overlap ratio has no effect on validity, but it has a significant influence on accuracy. The angle has a great impact on the validity and efficiency of ICP but has no effect on accuracy. The distance only affects validity, has a limited effect on efficiency and no effect on accuracy. Meanwhile, Gaussian noise has a little effect on the validity. In addition, the general effective range of point-to-point ICP is larger than that of point-to-plane ICP, but the point-to-plane ICP algorithm presents a better efficiency. The point-to-point ICP is more robust to Gaussian noise with respect to validity, while the point-to-plane is more resistant in terms of accuracy.

To address the registration problem in current machine vision, a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation is proposed. First, the feature information is comprehensively described using FPFH feature description and the local correlation of the feature information is established using greedy projection triangulation. Thereafter, the sample consensus initial alignment method is applied for initial transformation to implement initial registration. By adjusting the initial attitude between the two cloud points, the improved initial registration values can be obtained. Finally, the iterative closest point method is used to obtain a precise conversion relationship; thus, accurate registration is completed. Specific registration experiments on simple target objects and complex target objects have been performed. The registration speed increased by 1.1% and the registration accuracy increased by 27.3% to 50% in the experiment on target object. The experimental results show that the accuracy and speed of registration have been improved and the efficient registration of the target object has successfully been performed using the greedy projection triangulation, which significantly improves the efficiency of matching feature points in machine vision.

To address the matching challenge between the High Resolution Imaging Science Experiment (HiRISE) Digital Elevation Model (DEM) and the Mars Orbiter Laser Altimeter (MOLA) DEM, we propose a terrain matching framework based on the combination of point cloud coarse alignment and fine alignment methods. Firstly, we achieved global coarse localization of the HiRISE DEM through nearest neighbor matching of key Intrinsic Shape Signatures (ISS) points in the Fast Point Feature Histograms (FPFH) feature space. We introduced a graph matching strategy to mitigate gross errors in feature matching, employing a numerical method of non-cooperative game theory to solve the extremal optimization problem under Karush–Kuhn–Tucker (KKT) conditions. Secondly, to handle the substantial resolution disparities between the MOLA DEM and HiRISE DEM, we devised a smoothing weighting method tailored to enhance the Voxelized Generalized Iterative Closest Point (VGICP) approach for fine terrain registration. This involves leveraging the Euclidean distance between distributions to effectively weight loss and covariance, thereby reducing the results’ sensitivity to voxel radius selection. Our experiments show that the proposed algorithm improves the accuracy of terrain registration on the proposed Curiosity landing area’s, Mawrth Vallis, data by nearly 20%, with faster convergence and better algorithm robustness.

Highlights A multi-view 3D camera system was built to capture depth maps and reconstruct point clouds of complete pigs in breeding farms. Geometric and morphological features were extracted from the reconstructed point clouds for measuring phenotypic traits via 3D processing algorithms. The minimal mean absolute error of predicting liveweight was 4.19 kg. The mean absolute percentage errors of predicting chest and abdominal circumferences were 2.33% and 2.06%, respectively. Abstract. Manually measuring the phenotypic traits of pigs in breeding farms can be time- and labor-intensive and cause animal stress, and subjective manual measurement errors slow down the progress of desired traits and genetic selection across multiple breeding generations. The objectives of this study were 1) developing a multi-view 3D camera system to capture depth maps and reconstruct point clouds of complete pigs in breeding farms and 2) extracting geometric and morphological features from the reconstructed point clouds for predicting phenotypic traits. The proposed 3D scanning system was installed in a swine breeding farm, and 481 PIC Camborough® pigs in parent stocks were used with RGB-depth video acquisition. Core 3D processing algorithms merged point clouds from three camera views into one complete pig point cloud, including Iterative Closest Point, Fast Point Feature Histograms, Random Sample Consensus, and Sorting. Gaussian Mixture Clustering and point cloud slicing were implemented to extract critical features for predicting six traits: abdominal circumference, chest circumference, front body height, rear body height, ear-to-tail straight length, and ear-to-tail curve length. Three standard objects were used to test the system’s performance with mean absolute errors (MAE) of 0.04 to 0.07 m. The chest and abdominal circumference correlated highly (r = 0.74) with liveweight. After comparative studies of seven combinations of the six phenotypic traits, the best weight prediction performance (MAE = 4.19 kg) was obtained with the combination of the first four traits via the XGBoost regressor. The study highlighted the innovative applications of artificial intelligence and 3D computer vision for pig breeding and genetics selection. Keywords: Artificial intelligence, Automation, Depth sensing, Expert system, Precision livestock farming.

Abstract. In order to reconstruct the three-dimensional model of the soybean rapidly and efficiently, this paper analyzes the registration effect of point clouds of soybean plants from multiple angles. First, the Kinect V2 camera is used to obtain the point cloud information of soybean plants at 24 different angles, which means that, the images are taken at intervals of 15 degree angles. Second, the obtained point cloud information is subjected to preprocessing operations such as straight through filtering and outlier filtering. Thirdly, two representative point cloud registration algorithms are investigated, which include local features based FPFH (Fast point feature histograms) and probability distribution based NDT (Normal distributions transform). Meanwhile, the FPFH-ICP and NDT-ICP are used to perform pairwise registration for point cloud data at different perspectives. Finally, time cost and accuracy of the above two registration strategies are compared and analyzed. The experimental results show that the NDT-ICP soybean plant point cloud registration algorithm is superior to that of FPFH-ICP in terms of time-consuming, registration accuracy, and adaptation at different angles. At the same time, it is found that when the image acquisition interval between two point clouds is less than or equal to 45 degrees, the adaptability to different point cloud registration algorithms is more robust, and the registration error is less.