Search

The predatory mites Neoseiulus barkeri (Hughes) and the predatory thrips Scolothrips takahashii (Priesner) are known as potential biocontrol agents for the two-spotted spider mite Tetranychus urticae (Koch). These two predator species occur simultaneously on crops in agricultural ecosystems and are proved to be involved in life-stage specific intraguild predation. The intraguild prey may play a role in securing the persistence of the intraguild predators during food shortage periods. To understand the potential of intraguild prey as food source for intraguild predators in the N. barkeri and S. takahashii guild at low T. urticae densities, the survival, development and reproduction of both predators was determined when fed on heterospecific predators. The choice tests were conducted to determine the preference of the intraguild predator between the intraguild prey and the shared prey. Results showed that 53.3% N. barkeri and 60% S. takahashii juveniles successfully developed when fed on heterospecific predators. Female intraguild predators of both species fed on intraguild prey survived and laid eggs throughout the experiment. In the choice test, both intraguild predator species preferred their extraguild prey T. urticae. This study suggested that intraguild prey served as an alternative prey for intraguild predators prolonged survival and ensured the reproduction of intraguild predators during food shortage, ultimately decreasing the need for the continual release of the predators.

Predatory mites are important biological control agents for phytophagous mites and several small insect pests on vegetables, while the control efficiency is often limited by the changeable or fluctuating environment, especially under the threat of global warming. To mitigate the undesired impacts of high temperature environment, a high-temperature adapted strain (HTAS) of the predatory mite Neoseiulus barkeri (Hughes) was screened from a conventional strain (CS) by long-term heat acclimation (35°C) and regular heat hardenings (45°C). In this study, to investigate the molecular mechanisms of enhanced thermotolerance in HTAS N. barkeri, four mitogen-activated protein kinase (MAPK) pathway genes were identified from the predatory mite N. barkeri. The expression levels at different developmental stages showed four MAPK pathway genes were highest at adults and lowest at eggs or nymphs in both strains. Under 42℃ high temperature stress, the expression levels of four identified MAPK pathway genes increased rapidly at a short time in both N. barkeri strains and the magnitude of expression increase in HTAS N. barkeri was even more evident. In addition, the MAPK pathway genes expression in CS N. barkeri sharply decreased following longer exposure periods at 4 h, whereas the expression in HTAS N. barkeri remained elevated after 4 h. These results suggested that MAPK signaling pathway participated in the formation of thermotolerance in predatory phytoseiid mites, which provided new insights in promoting the biological control efficiency of predatory mites under changeable environment.

The whitefly Bemisia tabaci is a cosmopolitan, polyphagous agricultural pest that poses serious threats to agricultural production globally. Beauveria bassiana, an entomopathogenic fungi (EPF), cannot only directly kill a variety of pests but also inhibit the occurrence of pests as fungal endophytes within plants. In this study, we explored how the introduction of B. bassiana as endophytes into tomatoes (Solanum lycopersicum) affected the fitness and feeding behaviour of whiteflies. The results showed a significant difference in the fertility of B. tabaci on tomatoes treated with B. bassiana by root irrigation, B. bassiana by local leaf spraying, and 0.05% Tween 80 solution, among which the number of eggs laid was the lowest on tomatoes treated with B. bassiana by root irrigation. Furthermore, B. tabaci fed on tomatoes treated with EPF by root irrigation showed a significantly higher mortality rate in each stage than control plants treated with 0.05% Tween 80 by constructing an age-stage, two-sex life table. In addition, the intrinsic rate of increase (r), the finite rate of increase (λ) and the net reproductive rate (R0) of the B. tabaci population on tomatoes treated with EPF by root irrigation were all significantly lower than those of the control. Moreover, tomatoes treated by root irrigation had a significant negative effect on the feeding behaviour of B. tabaci by shortening the duration of watery salivation and phloem ingestion. Our results indicated that B. bassiana effectively inhibited the occurrence of whitefly as endophytes. These findings enhanced our understanding of the inhibition of endophytic EPF on pests.

A real-time imaging recognition and positioning method based on visible light communication flat light source is proposed. This method images the visible light communication flat light source through the rolling shutter effect of the complementary metal-oxide semiconductor imaging sensor and obtains the rectangular area outline of the light source. The light and dark stripe information of image with the digital image processing method realizes light source matching recognition by defining the concept, the autocorrelation sequence, which can be used to obtain the identity of the light source, and the rectangular vertex coordinate information of flat light source achieves high-precision vision positioning on the basis of inertial measurement unit attitude sensor-assisted imaging. Simultaneously, the corresponding positioning module is developed for positioning testing. The test results indicate that the plane positioning error is less than 4.5 cm, and the positioning frequency is greater than 10 Hz, which provides a high-precision visual positioning solution for indoor positioning.

Whitefly Bemisia tabaci is a polyphagous super pest, which causes serious threat to agricultural production globally. Beauveria bassiana, as entomopathogenic fungi (EPF), can not only directly kill a variety of pests, but also inhibit the occurrence of pests as fungal endophytes within plants. In this study, we explored how the introduction of B. bassiana as endophytes into tomatoes (Solanum lycopersicum) affected the fitness and feeding behavior of whitefly. The result showed that there was significant difference in the fertility of B. tabaci on tomatoes treated with B. bassiana by root irrigation, B. bassiana by local leaf spray, and 0.05% Tween 80 solution, among which the number of eggs laid was the lowest on tomatoes treated with B. bassiana by root irrigation. Furthermore, B. tabaci fed on tomatoes treated with EPF by root irrigation showed significantly higher mortality rate in each stage than control plants with 0.05% Tween 80, by constructing the age-stage, two-sex life table. In addition, the intrinsic rate of increase (r), the finite rate of increase (λ) and the net reproductive rate (R0) of B. tabaci population on tomatoes treated with EPF by root irrigation were all significantly lower than control. Moreover, tomatoes treated by root irrigation had a significant negative effect on the feeding behavior of B. tabaci, by shortening the watery salivation and phloem ingestion. Our results indicated that B. bassiana effectively inhibited the occurrence of whitefly as endophytes. These findings enhanced our understanding about the inhibition of endophytic EPF on pests.

The non-consumptive effects of predator-induced stress can influence a variety of life-history traits. Many previous studies focused only on short-term effects such as development and reproductive rates. Recent studies have showed that long-term predation stress (given during the whole life of the prey) and short-term predation stress (provided during the immature stage of the prey) could generate completely opposite results: the former could decrease lifespan, whereas the later could increase lifespan. However, it is still unclear whether the advantage is because of the short duration of exposure or the early stage of life during which exposure was exerted. Thus, in this study, the prey (Tyrophagus putrescentiae) was exposed to predation stress from the predator (Neoseiulus cucumeris) during different life stages (larva, protonymph, tritonymph, first 5 d of oviposition, the full lifespan or none of the above). The results showed that the predation stress supplied during larval and protonymphal stage delayed development, reduced fecundity and prolonged lifespan of the prey, while the stress given during tritonymphal stage only reduced lifespan slightly and the stress given during the first 5 d of oviposition did not change lifespan but reduced fecundity. This study indicated that the effects of predation stress are dependent on prey life stage and the predation stress experienced in the early life stages is important to lifespan modulation.

Sensor calibration is a fundamental step for improving the performance in sensor fusion, the aim of which is to spatially and temporally register sensors with respect to each other. This paper presents a high-accuracy autocalibration method to estimate extrinsic parameters between LiDAR and an IMU. LiDAR/IMU calibration is a challenging task since the raw measurements are distorted, biased, noisy, and asynchronous. Our calibration approach adopts continuous-time trajectory estimation wherein the IMU trajectory is modeled by Gaussian process(GP) regression with respect to the independent sampling timestamps. Accordingly, the distorted and delayed LiDAR points sampled at discrete timestamps can be analytically modeled in on-manifold batch optimization. To efficiently and accurately associate laser points with stable environmental objects, the method is carried out in known environments with a point map that is segmented as structured planes and managed by a specially designed octree map. We thoroughly investigated factors relevant to the calibration accuracy and evaluated the performance of the proposed method using both simulated and real-world datasets. The results demonstrate that the accuracy and robustness of our calibration approach are sufficient for most applications.

In this paper, a SLAM-integrated MMS with the capability of efficient, consistent and robust mapping in complex environments where GNSS signal is intermittently lost is presented. The system is developed to take the feedback from mapping module to the localization module into account, which significantly improves the smoothness of the 6 DOF trajectory estimation. In particular, the complex mapping problem is modeled as a hierarchical factor graph to improve the computation efficiency of the optimization back-end. Moreover, GNSS signal is modeled as a plug-and-play constrained factor to facilitate the mapping process in complex urban environments. We evaluate the performance of the proposed method on KITTI benchmark, and compare the result with both the GNNS/INS system and SuMa, which represents the traditional MMS and the typical laser SLAM system respectively. The results confirm that the proposed method enables normal and accurate mapping even when the inavailability of GNSS is up to 60%. It outperforms the traditional MMS in terms of map consistency with an increase of 24.7% on average MME, and outperforms laser SLAM in terms of the absolute accuracy with 5.9 times increase on average RMSE.

Scan registration is the basis of LiDAR-based mapping and localization. When a good initial guess is available, classical local registration algorithms such as iterative closet point (ICP) and normal distributions transform (NDT) can be utilized. However, scan registration without odometer or inertial measurement unit (IMU) is still an open problem. After reviewing the existing methods, we summarize a modular planar-feature-based registration framework. Based on the idea of modular design, we present a novel global scan registration method based on planar features. A modified renormalization-based algorithm considering the sensor noise model is derived and used for accurate plane fitting and covariance computing. An efficient adaptive plane segmentation algorithm is employed to extract planar patches which is based on voxel growing. A novel projected-image-based method is utilized for fast plane attribute calculation. The attributes of individual planes and relation constrains between the planes are employed to robustly build plane correspondences. The plane-plane singular value decomposition (SVD) method is used to estimate the transform, and normalization of the point coordinates is adopted to further improve accuracy. A consensus metric is introduced to determine plane correspondences and transform solution updating, and to select the optimal solution using the distance from the centroid of one plane to its conjugate plane. Extensive comparative experiments were performed on four datasets covering different structured scenarios to evaluate the performance of the proposed registration method. The experimental results demonstrate that the proposed method achieves a better balance between accuracy and efficiency, and has more stable and robust performance across different scenarios compared to all tested state-of-the-art algorithms.

In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd–Octree based descriptors as input for 3D pairwise point cloud matching. The classic pipeline of 3D point cloud registration involves two steps, viz., the point feature matching and the globally consistent refinement. We focus on the first step that can be further divided into three parts, viz., the key point detection, feature descriptor extraction, and pairwise-point correspondence estimation. In practical applications, the point feature matching is ambiguous and challenging owing to the low overlap of multiple scans, inconsistency of point density, and unstructured properties. To solve these issues, we propose the KdO-Net for 3D pairwise point feature matching and present a novel nearest neighbor searching strategy to address the computation problem. Thereafter, our method is evaluated with respect to an indoor BundleFusion benchmark, and generalized to a challenging outdoor ETH dataset. Further, we have extended our method over our complicated and low-overlapped TUM-lab dataset. The empirical results graphically demonstrate that our method achieves a superior precision and a comparable feature matching recall to the prior state-of-the-art deep learning-based methods, despite the overlap being less than 30 percent. Finally, we implement quantitative and qualitative ablated experiments and visualization interpretations for illustrating the insights and behavior of our network.

Background: The suicidal behaviors in patients with borderline personality disorder (BPD) have been extensively studied in western countries. However, few data are available on the suicidal behaviors, especially suicide attempt (SA), in Chinese BPD patients. Methods: 84 Chinese BPD patients were included and the following data were collected: the demographics, comorbidity of DSM-Ⅳ axis Ⅰ and axis Ⅱ diagnoses of mental disorders, times of hospitalization, and history of SA. All patients self-completed the Barratt Impulsiveness Scale-Ⅱ, Beck Depression Inventory-Ⅱ, Buss & Perry Aggression Questionnaire, Child Trauma Questionnaire-Short Form and Beck Hopelessness Scale. Results: 52 (61.9%) patients reported SA(s) during their lifetime. Univariate logistic regression analysis found eleven variables associated with SA in BPD patients, including female, unemployment, times of hospitalizations, major depressive disorder (MDD), motor impulsiveness, hostility, self-aggression, total score of aggression, depressive symptoms, emotional neglect and total score of abuse. Multivariate logistic regression analysis identified three correlates of SA from variables above: MDD, hostility, and self-aggression. Self-aggression showed a weak correlative trend. Conclusions: Chinese BPD patients are at a great risk of suicide; the treatment of MDD and reduction of aggression might be helpful for the suicide prevention in clinical management of Chinese BPD patients.

We address the issue of the semantic segmentation of large-scale 3D scenes by fusing 2D images and 3D point clouds. First, a Deeplab-Vgg16 based Large-Scale and High-Resolution model (DVLSHR) based on deep Visual Geometry Group (VGG16) is successfully created and fine-tuned by training seven deep convolutional neural networks with four benchmark datasets. On the val set in CityScapes, DVLSHR achieves a 74.98% mean Pixel Accuracy (mPA) and a 64.17% mean Intersection over Union (mIoU), and can be adapted to segment the captured images (image resolution 2832 ∗ 4256 pixels). Second, the preliminary segmentation results with 2D images are mapped to 3D point clouds according to the coordinate relationships between the images and the point clouds. Third, based on the mapping results, fine features of buildings are further extracted directly from the 3D point clouds. Our experiments show that the proposed fusion method can segment local and global features efficiently and effectively.

One of the main targets of the Internet of Things (IoT) is the construction of smart cities, and many industries based on the IoT serve popular applications in a smart city. However, 3-D reconstruction constitutes a major difficulty in the construction of a smart city. In recent years, oblique photography technology has been widely applied in the rapid 3-D modeling and other aspects of smart cities. However, in the automatic construction of a 3-D mesh model for oblique photogrammetry, complex building geometries make it very difficult to construct a triangular mesh model. Therefore, a network construction method is needed that can not only effectively construct a 3-D mesh model but also address the results of auto-modeling for an oblique image. The representative network construction method is a huge triangulation network in which the constructed surface of the object does not satisfy the manifold features and it is inconvenient to optimize and edit the model, yielding a low network construction efficiency. To solve these problems, a new method for constructing a high-quality manifold mesh model is proposed in this paper. First, an adaptive octree division algorithm is used to divide the point cloud data into sub domains that cover each other. Then, a mesh reconstruction is performed in each sub domain, and an efficient mesh construction algorithm based on relabeling the vertices of the directed graph is proposed to construct the manifold mesh. Finally, a triangular facet orientation method is used to homogenize the normal vectors of the mesh. The experimental results proof that the proposed method greatly improves the mesh reconstruction, effectively reflects the model details, and possesses a strong anti-noise ability. Also, it has a good robustness and is particularly suitable for the 3-D reconstruction of large scenes and complex surfaces.

The Ultra-Wide Bandwidth (UWB) signal has received much attention due to its penetrability and high positioning accuracy with the increasing demand for indoor positioning, in which Global Positioning System (GPS) signal is challenging. In practice, there are two main problems with indoor 3D positioning based on UWB. One is that the quality of Time Difference of Arrival (TDOA) measurements varies in different observation environments. Namely, the time delay generated by Non-Line-of-Sight (NLOS) causes an enormous deviation from the real distance and cannot be well distinguished from the measurement reducing the accuracy of positioning. The other problem is that the height estimates, which are calculated using the conventional least square method, are extremely unstable due to the limitation of the Base Station (BS) layout. To address these problems, this paper presents Robust Ridge Estimation (RRE) for UWB positioning. Firstly, NLOS errors are detected, and the weights of each measurement are automatically adjusted in accordance with their quality, which is represented by the residuals between the estimated measurements and real observations. Then, the ridge estimation algorithm is applied iteratively for position estimation based on a robust estimation framework, which updates the weight of the measurements at each iteration. This approach transforms unbiased estimation to biased estimation by adding constraints that minimize the weighted quadratic sum of some parameters. As a result, the impact of NLOS can be reduced. The experimental result shows an improvement of RMSE in positioning with 45.71% when compared with ridge estimation in an NLOS/Line-of-Sight (LOS) mixed environment and an increase of robustness to NLOS with 56.11%.

Precise localization of semantic segmentation is attracting increasing attention, and salient performances are dominated by deep learning-based methods, especially deep convolutional neural networks (DCNNs). However, the outputs from the final layer of DCNNs are not sufficiently localized for accurate object boundaries due to their invariance properties, which makes precise boundary recovery of semantic segmentation an academically challenging question. Both 2D and 3D objects suffer from the same problem. Considering this, this paper conducts a comprehensive survey of precise boundary recovery for semantic segmentation, focusing mainly on 2D images and 3D point clouds. Firstly, we formulate the problem of potential boundary recovery for semantic segmentation based on DCNNs, elaborate on the terminology as well as background concepts in this field. Then, we categorize boundary recovery methods into four strategies according to their techniques and network architectures to discuss how they obtain accurate boundaries of semantic segmentation. Next, publicly available datasets on which they have been assessed are argued. To compare these datasets, we design diagrams based on five indicators to help researchers judge which are the ones that best suit their tasks. Moreover, we further compare and analyze the performance of all the reviewed methods through experimental results. Finally, current challenges and prospective research issues are discussed extensively.

Light detection and ranging equipment (LiDAR) has wide applications in the field of mobile surveying, autonomous driving, unmanned aerial vehicle and military, etc., since its abilities of fast three-dimensional environmental information acquisition, robustness to variable illumination conditions as well as wide measurement range. The main difficulty for localization and mapping based on LiDAR is the registration between successive point clouds, which is caused by the continuous motion and undetermined trajectory of LiDAR. A real-time dead reckoning and three-dimensional environment mapping method based on 3D-LiDAR is presented in this paper. The proposed system adopts a divide and parallel method, which performs high frequency pose estimation and low frequency mapping on parallel threads to ensure the real-time performance. In point preprocess section, the efficient elimination of point cloud distortion is processed and feature points are extracted. In dead reckoning section, point cloud registration employs generalized iterative closest point (GICP) algorithm which increases accuracy of registration by taking local covariance information of each point into consideration. In environmental mapping section, the multi-channel GICP (MCGICP) algorithm which adds point cloud intensity information into GICP framework is applied to align local point cloud with the global point cloud map. The robustness and convergence are promoted, since the fusion of additional point in-formation. Finally, the performance of the proposed approach is evaluated by the KITTI dataset. The experimental results show that our proposed simultaneous dead reckoning and 3D environment mapping method based on 3D laser scanner is an effective and feasible on-line solution which achieves high accuracy under 1% in all kinds of environment scenarios. Moreover, the ability of self-localization and real-time mapping is improved dramatically in those environments with poor geometric feature patterns.

Background/Aims: Serum 25-hydroxyvitamin D [25(OH)D] levels proved to be associated with prognosis of patients with colorectal cancer or breast cancer, but its prognostic role in hematological malignancies was still unclear. A systematic review and meta-analysis was performed to comprehensively evaluate the association between serum 25(OH)D levels and prognosis of patients with hematological malignancies. Methods: We searched Pubmed, Embase, Web of Science, and Google Scholar for studies evaluating the association between serum 25(OH)D levels and prognosis of patients with hematological malignancies. The hazard ratios (HR) with 95% confidence intervals (95%CI) for overall survival (OS) or relapse-free survival (RFS) were pooled using meta-analysis. Results: Seven studies with a total of 2,643 patients with hematological cancer were finally included into the meta-analysis. Overall, compared with normal serum 25(OH)D levels, low serum 25(OH)D levels were significantly associated with both poorer OS (HR = 1.85, 95% CI 1.54-2.23, P

Nonalcoholic fatty liver disease (NAFLD) refers to a pathological condition of hepatic steatosis. Insulin resistance is believed to be the key mechanism mediating initial accumulation of fat in the liver, resulting in hepatic steatosis. Kukoamine A (KuA), a spermine alkaloid, is a major bioactive component extracted from the root barks of Lycium chinense (L. chinense) Miller. In the current study, we aimed to explore the possible effect of KuA on insulin resistance and fatty liver. We showed that KuA significantly inhibited the increase of fasting blood glucose level and insulin level, and the glucose levels in response to glucose and insulin load in HFD-fed mice, which was in a dose-dependent manner. KuA dose-dependently decreased the histological injury of liver, levels of hepatic triglyceride (TG), and serum AST and ALT activities in HFD-fed mice. The increase of serum levels of TNFɑ, IL-1β, IL-6 and C reactive protein in HFD-fed mice was inhibited by KuA. HFD feeding-induced increase of hepatic expression of Srebp-1c and its target genes, including fatty acid synthase (FAS) and acetyl CoA carboxylase 1 (ACC1), was significantly inhibited by KuA. Moreover, upregulation of Srebp-1c notably inhibited KuA-induced improvement of insulin-stimulated glucose uptake, decrease of lipid accumulation and H2O2 level in palmitic acid-treated AML-12 cells. In conclusion, we reported that KuA inhibited Srebp-1c and downstream genes expression and resulted in inhibition of lipid accumulation, inflammation, insulin resistance and oxidative stress. Overall, our results provide a better understanding of the pharmacological activities of KuA against insulin resistance and hepatic steatosis.

With the rapid development of national economy, higher requests have been put forward on PNT (positioning navigation and timing) service. Researches on integrated navigation systems, including fusion technologies of multi-navigation sources and adaptive algorithms for various application scenarios, have become the highlighted topic. The method of laboratory calibration for GNSS/INS/LiDAR integrated navigation system, applied for indoor and outdoor seamless positioning, was studied in this paper. Combined with high-precision Theodolite and Laser Scanning System, this paper proposed a laboratory high-accuracy calibration scenario for integrated navigation system, designed a rapid laboratory calibration model, deduced the corresponding error model with the analysis of differential equations and the method of Monte Carlo, and finished the lever-arm calibration of GNSS and the mounting parameters of laser scanner, and designed a rapid laboratory calibration model. Laboratory experiments had been carried out to verify the feasibility of this millimeter-level calibration method, which could be used for calibrating the initial mounting parameters of the integrated navigation system.

This paper presents a characterization of the Sick LMS511-20100Pro laser range finder. With high accuracy and good robustness, this range sensor is suitable for various mobile robotic applications both indoors and outdoors. However, very few studies concerning the performance characterization of the LMS511-20100Pro can be consulted for better understanding and utilizing this sensor in practice. Therefore, some factors that could influence the sensor performance, such as drift effect, target distance, angular resolution, target material and mixed pixel, were tested and further analyzed. The effect of target distance and material on intensity information was also investigated. All the performed experiments demonstrate that the performance of the LMS511-20100Pro exceeds the specified values and can meet the requirements of simultaneous localization and mapping, although its practical performance might be significantly affected by some certain factors.

Mesenchymal stem cells (MSCs) play important roles in tissue regeneration due to their self-renewal, multilineage differentiation and immunosuppression abilities. MSCs can be isolated from various kinds of tissue, such as umbilical cord, cord blood and placenta. Human placenta mesenchymal stem cells (hPMSCs) possess stronger immunosuppressive properties, such as the ability to inhibit T-cell activation and proliferation, than human bone marrow MSCs. We have investigated that the roles of the programmed death ligands 1 and 2 (PDL1 and PDL2) in hPMSC adhesion, migration and immunosuppression were investigated. PDL1 and PDL2 were highly expressed by hPMSCs. Knockdown of PDL1 and/or PDL2 by siRNA increased hPMSC adhesion, but greatly decreasing migration. PDL1 and PDL2 expressed on hPMSCs inhibited T-cell proliferation by arresting the cell cycle. Knockdown of PDL1 and/or PDL2 in hPMSCs, however, had no effect on the expression of CD69, a T-cell early activation marker found on both CD4(+) and CD8(+) T-cell subsets. In summary, the roles of the negative co-stimulators PDL1 and PDL2 is on the adhesion, migration and immunosuppression of hPMSCs. These findings may be useful regarding the potential use of hPMSCs in clinical cell.

Automatic multistation registration of laser-scanned point clouds is a research hotspot in laser-scanned point clouds registration. Some targets such as common buildings have plenty of planar features, and using these features as constraints properly can bring about high accuracy registration results. Starting from this, a new automatic multistation registration method using homologous planar features of two scan stations was proposed. In order to recognize planes from different scan stations and get plane equations in corresponding scan station coordinate systems, k-means dynamic clustering method was improved to be adaptive and robust. And to match the homologous planes of the two scan stations, two different procedures were proposed, respectively, one of which was based on the “common” relationship between planes and the other referenced RANSAC algorithm. And the transformation parameters of the two scan station coordinate systems were calculated after homologous plane matching. Finally, the transformation parameters based on the optimal match of planes was adopted as the final registration result. Comparing with ICP algorithm in experiment, the method is proved to be effective.