A novel robust AE/MS source location method using optimized M-estimate consensus sample

Due to the complexity of the real engineering environment, the arrival measurement inevitably contains outliers and leads to serious location errors. In order to eliminate the influence of the outliers effectively,this paper proposes a novel robust AE/MS source localization method using optimized M-estimate consensus sample. First, a sample subset is selected from the entire arrival set to obtain fitting model and its parameters. Second, consensus set is determined by checking the arrivals with the fitting model instantiated by the estimated model parameters. Third, optimization process is performed to further optimize the consensus set. The above steps are iterated, and the final source coordinates are obtained by using all the elements in the optimal consensus set. The novel method is validated by a pencil-lead breaks experiment. The results indicate that the novel method has better location accuracy of less than 5 mm compared to existing methods, regardless of the presence or absence of outliers. With the increase of outlier scale and outlier ratio, the location result of the proposed method is always more stable and accurate than that of the existing methods. Mine blasting experiments further demonstrate that the new method holds good prospects for engineering applications.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

Research on color image matching method based on feature point compensation in dark light environment

Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.

基于最佳几何约束和RANSAC的特征匹配算法

为解决特征点匹配的质量与计算效率不能兼得的问题,研究了一种基于最佳几何约束和RANSAC(random sample consensus)的特征点匹配方法。采用KNN(k-nearest neighbor)算法对提取到的特征点完成初始匹配,根据匹配点对连接线长度相等、斜率相同的特点,基于统计排序策略构建最佳几何约束,剔除明显错误匹配。利用RANSAC算法进行二次过滤,确保特征匹配点对的正确率,同时给出实验结果加以验证。结果表明:在正常光照下,与Lowe’s算法和GMS算法相比,该算法匹配到的点对数有了明显增加,同时很大程度上保证了特征点的质量。

基于图像特征的双目测距系统的研究

针对双目视觉测距中测量误差大、图像信息单一、实时性差等问题,提出一种基于ORB(oriented fast and rotated brief)特征的双目测距方法。对视频帧进行中值滤波处理,提取图像ORB特征,通过实验选出匹配效果最好的汉明距离。对筛选后的匹配点进行RANSAC(random sample consensus)模型估计,去除误匹配,分析视差和真实距离的模型关系,构建最优的测距模型并在实验平台上进行验证。结果表明:所提方法比其他双目测距方法具有测距精确、运行速度快、鲁棒性强的优势,能够实时显示图中特征的距离信息。

3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

Science Letters:Visual odometry for road vehicles—feasibility analysis

Estimating the global position of a road vehicle without using GPS is a challenge that many scientists look forward to solving in the near future. Normally, inertial and odometry sensors are used to complement GPS measures in an attempt to provide a means for maintaining vehicle odometry during GPS outage. Nonetheless, recent experiments have demonstrated that computer vision can also be used as a valuable source to provide what can be denoted as visual odometry. For this purpose, vehicle motion can be estimated using a non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The results prove that the detection and selection of relevant feature points is a crucial factor in the global performance of the visual odometry algorithm. The key issues for further improvement are discussed in this letter.

A Robust Model Fitting-based Method for Transmission Line Extraction from Airborne LiDAR Point Cloud Data

Airborne Light Detection And Ranging(LiDAR)can provide high-quality three-dimensional information for the safety inspection of electricity corridors.However,the robust extraction of transmission lines from airborne point cloud data is still greatly challenging.Therefore,this paper proposes a robust transmission line extraction method based on model fitting from airborne point cloud data.First,the candidate power line generation method based on height information is used to reduce the computational complexity at the subsequent steps and the false positives in the extracted results.Then,on the basis of the block-and-slice-constraint Euclidean clustering,a linear structure recognition method based on RANdom SAmple Consensus(RANSAC)is proposed to produce the initial individual transmission line components.Finally,a robust nonlinear least square-based fitting method is developed for the individual transmission line to generate the parameters of its mathematical model for further optimizing the extraction.Experiments were performed on LiDAR point cloud data captured from the helicopter and Unmanned Aerial Vehicle(UAV)platform.Results indicate that the proposed method can efficiently extract the different types of transmission lines along electricity corridors,with the average precision of approximately 98.1%,the average recall of approximately 95.9%,and the average quality of approximately 94.2%,respectively.

基于点云空间索引的隧道断面提取方法

探索了一种基于三维激光点云空间索引的隧道断面提取方法。该方法主要通过建立空间索引,利用k维树(kdimensional tree,KD树)和规则格网提取频数分布特征,采用含有多种几何模型约束条件的随机抽样一致(random sample consensus,RANSAC)算法完成断面拟合。实验结果表明,该方法对隧道点云的平均降噪率达到97%,能准确提取隧道断面。

基于多重单应性变换的改进ViBe算法

针对目前运动目标检测领域内较流行的ViBe算法不适用于动态相机的问题,根据平面间变换为单应性变换的事实,提出了一种基于多重单应性变换的改进ViBe算法。利用ORB(oriented fast and rotated brief)特征点匹配与随机抽样一致(random sample consensus,RANSAC)算法,计算得到相邻帧的不同平面的变换关系;采用全局光流补偿算法消除运动目标上的特征匹配点对,避免多重单应性变换中包含运动平面的变换;根据多重单应性变换改进ViBe算法的前景判定与背景模型更新,使改进算法适用于运动相机采集的数据。在公开视频数据集CDnet 2014与Complex Background Dataset上进行了相关实验,实验结果证明,改进算法能在尽量保留真实运动目标区域的同时,大幅度消除相机运动带来的影响,且在精度、特异性以及虚警率上表现出色,实时处理效率能达到20帧/s。

基于激光点云的长方体长宽高计算

为了从海量三维激光扫描点云数据中自动提取所需物体信息,介绍了一种自动提取长方体长宽高的方法。首先,截取某一包含前后左右面线的断面并使用随机抽样一致(random sample consensus,RANSAC)算法提取不同面线,再通过不同面线对长方体的不同面点分类;通过拟合平面,建立标准坐标系,并将不同面点转换到标准坐标系下;然后可通过3种方法计算长方体的长宽高:边界线交点法、多面相交法及对立面距离法;最后,比较自动化计算结果与正确结果,验证了这3种计算方法的正确性。

基于改进RANSAC算法的全景图像拼接技术

现实场景中相机获取的图像视角范围往往是有限的,而实际需求又要求得到场景的全景图,针对日常生活和工业生产中对全景图像的需求以及传统的RANSAC(random sample consensus)算法在图像配准环节因为迭代次数没有上限导致出现误匹配点对且配准速度不高的缺陷,提出了一种改进RANSAC算法来提高全景图像拼接的效率。改进RANSAC算法通过检测圆内的点来寻找一个最优数据检测模型,并通过粒子群算法不断更新迭代圆心的坐标,最终得到一个最佳的匹配模型,消除特征点匹配环节出现的异常值,在提高特征点配准的准确率的同时降低算法复杂度。在对多组图像进行拼接的实验表明,本文提出的改进RANSAC算法相较于其他几种算法平均正确匹配率提高了9.0575%,同时算法的平均配准速率提高了5.17375 s,实现了较鲁棒的全景图像拼接效果。

一种RANSAC多模型拟合的隧道点云滤波算法

针对目前大多数点云滤波方法的不足,提出基于随机抽样一致(random sample consensus, RANSAC)多模型拟合的隧道点云滤波算法。与单模型拟合滤波方法不同,该算法在每个隧道横断面上使用多个圆模型进行分段拟合,多个模型具有不同的模型参数,将远离所拟合的各圆模型的点作为噪声点进行剔除。实验结果表明,相较于单模型拟合滤波方法,所提出的多模型法具有更低的一类误差和相近的二类误差,对隧道三维激光点云的滤波效果更好。

一种改进选点策略的RANSAC圆柱面检测算法

基于随机抽样一致(random sample consensus,RANSAC)任选点作为种子点计算参数,计算圆柱面检测初始值,不能保证选取的两个点的质量,影响计算得到的模型参数,且在该参数下继续对其余点进行局内外点的判断,会影响效率。针对此问题,改善了种子点的选取规则,提出了一种基于3个点及其法向量构造初始圆柱面方程参数值的方法。结果表明,所提方法有效提高了圆柱面模型初始值的质量,根据给定规则,提高了得到最优模型参数的概率和效率。

Method for Visual Localization of Oil and Gas Wellhead Based on Distance Function of Projected Features

A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.

A monocular visual measurement system for UAV probe-and-drogue autonomous aerial refueling

Purpose–The purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling(AAR)for unmanned aerial vehicle,which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.Design/methodology/approach–Methods and techniques for marker detection,feature matching and pose estimation have been designed and implemented in the visual measurement system.Findings–The simple blob detection(SBD)method is adopted,which outperforms the Laplacian of Gaussian method.And a novel noise-elimination algorithm is proposed for excluding the noise points.Besides,a novel feature matching algorithm based on perspective transformation is proposed.Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.Practical implications–The visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.Originality/value–The SBD method is used to detect the features and a novel noise-elimination algorithm is proposed.Besides,a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.