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A machine vision-based radial circular runout measurement method

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第9-10期126卷 3949-3958页

作者： Li, Wenjie Li, Fuquan Jiang, Zhansi Wang, Haijian Huang, Yang Liang, Qiaoxin Huang, Meikuan Li, Tengfei Gao, Xingyu Guilin Univ Elect Technol Sch Mech & Elect Engn Guilin 541004 Peoples R China Zhuhai Huaya Machinery Technol Ltd Co 519000 Zhuhai 519060 Peoples R China Guangxi Technol Coll Machinery & Elect Nanning 530007 Peoples R China

In order to overcome the disadvantages of contact measurement, time-consuming in traditional radial circular runout measurement, a non-contact measurement method based on machine vision technology is proposed in this paper. Using the image template matching and sub-pixel edge extraction techniques, the fine edge information of measured region is obtained. The rand sample consistency (ransac) algorithm is performed to filter the noise of edge data. Then, the least squares method is used for the parallel line fitting of two edges of measured region, and the reference axis of the shaft is gotten. The distance of measured point to the reference axis is defined as the radius at this rotating position, and the radius fluctuation at different rotational positions is the radial circular runout. The proposed method also allows random rotation of the measured shaft during the measuring process, and the high-precision-assisted rotation equipment is not essential, which enhances the convenience of the measuring equipment.

关键词： Machine vision Radial circular runout ransac algorithm Least squares method

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An Improved ransac Registration algorithm Based On Region Covariance Descriptor

An Improved RANSAC Registration Algorithm Based On Region Co...

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Chinese Automation Congress (CAC)

作者： Han, Jie Wang, Fei Guo, Yu Zhang, Chuanhao He, Yicong Xi An Jiao Tong Univ Xian Shaanxi Provinc Peoples R China

ISBN: (纸本)9781467371896

Point clouds registration is one of the key parts in 3D model reconstruction. Random Sample Consensus (ransac) is a typical algorithm for coarse registration, which can provide initial values for accurate registration methods such as ICP. In this paper, we propose an improved ransac algorithm based on 3D region covariance descriptor(RC_ransac). Region covariance descriptor of each point in a down sampled point cloud is established. The region covariance descriptor extracts the statistical information of neighborhood for each point. Region covariance descriptor has more significant distinction than Euclidean distance adopted by classical ransac and another improved ransac algorithm, called M_ransac. The accuracy can be improved by significant distinction. Additionally, the region covariance descriptor with compact structure is efficient in time. Experimental results on 3D point clouds registration show that the RC_ransac has superior performance to ransac and M_ransac in computational complexity and accuracy.

关键词： point cloud registration ransac algorithm region covariance descriptor

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Monocular Visual Odometry Method for Indoor Mobile Robots 1

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20th Chinese Intelligent Systems Conference

作者： Cao, Fan Guangdong Univ Technol Guangzhou 510006 Peoples R China

ISBN: (数字)9789819786503

ISBN: (纸本)9789819786527;9789819786503;9789819786497

With the recent advancements in artificial intelligence, various intelligent robots, including floor cleaning robots, surgical robots, and industrial robots, have become increasingly prevalent in our daily work and lives. These robots require not only a high level of intelligence but also a keen perception of their surroundings. This research is dedicated to the design of a monocular visual odometer system tailored to the navigation and positioning requirements of low-cost indoor mobile robots. The monocular vision odometer offers a simple yet cost-effective and realtime solution, enabling stable operation of mobile robots within indoor environments. To address the task of monocular visual image depth estimation, we introduce a novel monocular visual odometry system in this research. In our system, we propose an ORB algorithm that integrates scale information. Moreover, to overcome the issue of feature points' main direction instability in traditional ORB algorithms, we incorporate grayscale information surrounding the feature points and employ a new window model to determine the main direction, thus enhancing stability. In the feature matching phase, we used the random sampling consistency (ransac) algorithm and make improvements to reduce its computational time. Finally, we conduct experiments to validate the effectiveness of the proposed algorithm. The experimental outcomes demonstrate that our method significantly enhances camera pose recovery accuracy while maintaining real-time performance for the mobile robot system.

关键词： monocular visual odomet scale space ORB algorithm ransac algorithm

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Research on Adaptive ORB-SURF Image Matching algorithm Based on Fusion of Edge Features

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IEEE ACCESS 2022年 10卷 109488-109497页

作者： Han, Leng Wang, Jiawei Zhang, Yi Sun, Xia Wu, Xuhui Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China Chongqing Inst Engn Chongqing 400056 Peoples R China

The traditional speeded-up robust features (SURF) algorithm has certain stability in scale, rotation, illumination and other changes. However, this algorithm has problems such as large amount of computation, low matching accuracy, and time-consuming in feature extraction and feature matching. An improved adaptive ORB-SURF image matching algorithm is proposed in this paper. Fusing edge features and using improved Oriented FAST and Rotated BRIEF (ORB) algorithms are used by this algorithm to extract image feature points. Moreover, SURF descriptors are used by feature points for feature description. Then an improved fast library for approximate nearest neighbors (FLANN) algorithm was used for adaptive feature matching. The random sample consensus (ransac) algorithm is used to eliminate the false matching point pairs after the selected points to be matched. Finally, the excellent matching point pairs reserved by the adaptive FLANN algorithm are combined with the excellent matching point pairs reserved by the ransac algorithm to complete the matching. The experimental results show that the average accuracy of the improved algorithm can reach more than 98%, which is about 6% higher than the original SURF algorithm. And the average matching time is 1.2S, which is about 25% lower than the original SURF algorithm. It is worth mentioning that the problem that the original SURF algorithm cannot predict the number of feature points through Hessian threshold is solved by this algorithm. Moreover, compared with the SuperPoint based deep learning image matching algorithm, the image matching time of this algorithm is reduced by 80%.

关键词： Feature extraction Image edge detection Prediction algorithms Approximation algorithms Image matching Lighting Image registration SURF algorithm edge detection image matching ORB algorithm FLANN algorithm ransac algorithm

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Design and experiment of visual navigated UGV for orchard based on Hough matrix and ransac

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International Journal of Agricultural and Biological Engineering 2021年第6期14卷 176-184页

作者： Mingkuan Zhou Junfang Xia Fang Yang Kan Zheng Mengjie Hu Dong Li Shuai Zhang College of Engineering Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River Ministry of Agriculture and Rural AffairsWuhan 430070China

The objective of this study was to develop a visual navigation system capable of navigating an unmanned ground vehicle(UGV)travelling between tree rows in the outdoor *** far,while most research has developed algorithms that deal with ground structures in the orchard,this study focused on the background of canopy plus sky to eliminate the interference factors such as inconsistent lighting,shadows,and color similarities in *** at the problem that the traditional Hough transform and the least square method are difficult to be applied under outdoor conditions,an algorithm combining Hough matrix and random sample consensus(ransac)was proposed to extract the navigation *** the image segmentation stage,this study used an H-component that was adopted to extract the target path of the canopy plus ***,after denoising and smoothing the image by morphological operation,line scanning was used to determine the midpoint of the target *** navigation path extraction,this study extracted the feature points through Hough matrix to eliminate the redundant points,and ransac was used to reduce the impact of the noise points caused by different canopy shapes and fit the navigation *** path acquisition experiment proved that the accuracy of Hough matrix and ransac method was 90.36%-96.81%and the time consumption of the program was within 0.55 s under different sunlight *** method was superior to the traditional Hough transform in real-time and accuracy,and had higher accuracy,slightly worse real-time compared with the least square ***,the OPENMV was used to capture the ground information of the *** experiment proved that the recognition rate of OPENMV for identifying turning information was 100%,and the program running time was 0.17-0.19 *** experiments showed that the UGV could autonomously navigate the rows with a maximum lateral error of 0.118 m and realize the automatic turning of the *** algorithm sat

关键词： orchard visual navigation unmanned ground vehicle Hough matrix ransac algorithm H-component

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Grey wolf assisted SIFT for improving copy move image forgery detection

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EVOLUTIONARY INTELLIGENCE 2022年第2期15卷 1097-1108页

作者： Devi, Moka Uma Babu, Uppu Ravi Acharya Nagarjuna Univ Guntur 522510 Andhra Pradesh India DRK Eng Coll & Technol Hyderabad 500043 India

Copy-move forgery is a general widespread type of digital image forgery, where a segment of an image is attached into a new portion of the similar image to hide or replicate the parts which are called forgered image. The forgered image appears original, as the objective region in spite of being forged, has attained the fundamental qualities of the similar image itself. The capability of the copy-move forgery detection (CMFD) technique is lacked due to some post-processing functions, like JPEG compression scaling, or rotation, etc. Therefore, this paper intends to develop a CMFD using Scale-invariant feature transform (SIFT), best-fin-first algorithm (BBF) and RANdom SAmple Consensus (ransac) directed by grey wolf optimization (GWO) algorithm. Initially, the keypoints are selected using SIFT principle, and BBF algorithm identifies the matched keypoints using keypoint threshold. Further, SIFT feature descriptor is determined, and the final extracted paired keypoints are given to ransac algorithm to remove all the mismatched keypoints. In this CMFD model, the parameters such as parameters keypoint threshold, maximum distance of inliers in ransac and distance threshold in SIFT features are optimized using GWO. The foremost purpose of this research work is maximizing the number of paired keypoints. Hence the proposed model is termed as GWO-based parameter optimization for CMFD (GWPO-CMD). The proposed model is compared over several other meta-heuristic-based keypoint threshold selections and proves its efficiency through diverse analysis.

关键词： Copy-move forgery detection SIFT features Keypoint threshold Best-bin-first GWO algorithm ransac algorithm

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Fast Positioning Method of Truck Compartment Based on Plane Segmentation

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 774-778页

作者： Zou, Wenye Shen, Dayong Cao, Puzhen Lin, Chengyang Zhu, Jiangtian Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410003 Peoples R China Changsha Ruxin Intelligent Technol Co Ltd Minist Sci & Technol Changsha 410013 Peoples R China

To improve the loading efficiency and shorten the loading time, a method based on LIDAR point cloud segmentation for fast positioning of trucks is proposed for the scenario of level-push automatic loading, in which the truck position and attitude are random. The point cloud data are collected by LIDAR and pre-processed by traditional methods in the order of straight pass filtering, voxel filtering, and plane segmentation. Four planar regions are segmented using the region growth algorithm, and then planar fitting is performed for each of these four regions, which requires a large number of iterations and complex calculations by the traditional planar segmentation method. Focusing on these drawbacks this paper first uses the region growing algorithm to cluster and group the matching points, then combines the least squares and ransac algorithms for plane fitting optimization to calculate the intercept of each plane, and finally uses these intercept values to perform spatial geometry calculations to obtain the exact position and locus of the carriage. Experiments show that the method reduces the number of iterations and improves the computational efficiency compared with the traditional ransac algorithm, thus improving the efficiency and accuracy of the plane fitting algorithm.

关键词： Automatic vehicle loading LIDAR ransac algorithm planar segmentation

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A Feature Line Extraction Method for Building Roof Point Clouds Considering the Grid Center of Gravity Distribution

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REMOTE SENSING 2024年第16期16卷 2969-2969页

作者： Yu, Jinzheng Wang, Jingxue Zang, Dongdong Xie, Xiao Liaoning Tech Univ Sch Geomat Fuxin 123000 Peoples R China Liaoning Tech Univ Collaborat Innovat Inst Geospatial Informat Serv Fuxin 123000 Peoples R China Chinese Acad Sci Inst Appl Ecol Key Lab Environm Computat & Sustainabil Liaoning P Shenyang 110016 Peoples R China

Feature line extraction for building roofs is a critical step in the 3D model reconstruction of buildings. A feature line extraction algorithm for building roof point clouds based on the linear distribution characteristics of neighborhood points was proposed in this study. First, the virtual grid was utilized to provide local neighborhood information for the point clouds, aiding in identifying the linear distribution characteristics of the center of the gravity points on the feature line and determining the potential feature point set in the original point clouds. Next, initial segment elements were selected from the feature point set, and the iterative growth of these initial segment elements was performed by combining the ransac linear fitting algorithm with the distance constraint. Compatibility was used to determine the need for merging growing results to obtain roof feature lines. Lastly, according to the distribution characteristics of the original points near the feature lines, the endpoints of the feature lines were determined and optimized. Experiments were conducted using two representative building datasets. The results of the experiments showed that the proposed algorithm could directly extract high-quality roof feature lines from point clouds for both single buildings and multiple buildings.

关键词： feature line extraction point clouds virtual grid eight-neighborhood ransac algorithm

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Bolt looseness detection algorithm based on 2D color space extraction and 3D depth measurement 17

Bolt looseness detection algorithm based on 2D color space e...

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17th Conference on Industrial Electronics and Applications (ICIEA)

作者： Yang, Ran Guan, Ziling Lu, Jiang Zhao, Yijing Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

ISBN: (纸本)9781665409841

This project mainly uses 2D and 3D image processing technology, and develops fault detection algorithm accordingly. The research object we focused on is bolt. In reality, as for the images processing, the shooting environment is complex with different shooting angles, and the change of light in the environment will change the RGB value of the same point. Target detection based on model training is used in the processing. The bolt fault detection is based on the anti-loose line detection model based on two-dimensional image processing. In order to reduce Error detection rate, the color threshold dictionary is introduced to get the optimal threshold, and the bolt depth detection model based on three-dimensional point cloud processing is introduced to jointly complete the fault detection of target faults.

关键词： labellmg YOLO-V4 Object Detection CIoU Loss mean Average Precision Edge detection Mathematical morphology color threshold dictionary point cloud library ransac algorithm

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Random Finite Set based Safe Landing Zone detection and tracking 13

Random Finite Set based Safe Landing Zone detection and trac...

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13th Asian Control Conference (ASCC)

作者： Jeong, Hyeon-Mun Lee, Woo-Cheol Choi, Han-Lim Dept Aerosp Engn Daejeon South Korea

ISBN: (纸本)9788993215236

This paper addresses a robust Safe Landing Zone (SLZ) perception method for the autonomous landing of UAV. First, a detection method for identifying SLZs is presented, which makes use of the ransac plane fitting algorithm and several constraints. Meanwhile, the inherent uncertainties in the navigational and perception sensors of UAV make SLZ detection and tracking difficult. Therefore, in this paper, the state of SLZs and UAV is modeled as a Random Finite Set, and the tracking process is implemented using the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. The performance of the proposed systems is demonstrated in the GAZEBO simulation environment's volcano map.

关键词： Safe Landing Zone ransac algorithm Random Finite Set modeling GM-PHD filter

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