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Real-time camera orientation estimation based on vanishing point tracking under Manhattan World assumption

JOURNAL OF REAL-TIME IMAGE PROCESSING 2017年第4期13卷 669-684页

作者： Elloumi, Wael Treuillet, Sylvie Leconge, Remy Univ Orleans Lab PRISME 12 Rue Blois F-45067 Orleans France

This paper proposes a real-time pipeline for estimating the camera orientation based on vanishing points for indoor navigation assistance on a Smartphone. The orientation of embedded camera relies on the ability to find a reliable triplet of orthogonal vanishing points. The proposed pipeline introduces a novel sampling strategy among finite and infinite vanishing points with a random sample consensus-based line clustering and a tracking along a video sequence to enforce the accuracy and the robustness by extracting the three most pertinent orthogonal directions while preserving a short processing time for real-time application. Experiments on real images and video sequences acquired with a Smartphone show that the proposed strategy for selecting orthogonal vanishing points is pertinent as our algorithm gives better results than the recently published RNS optimal method, in particular for the yaw angle, which is actually essential for the navigation task.

关键词： Vanishing point Real-time camera orientation ransac algorithm Navigation assistance Smartphone

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Comparison of estimation algorithms for individual tree diameter at breast height based on hand-held mobile laser scanning

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SCANDINAVIAN JOURNAL OF FOREST RESEARCH 2021年第6期36卷 460-473页

作者： Fan, Weiwei Liu, Haoran Xu, Yongsheng Lin, Wenshu Northeast Forestry Univ Coll Engn & Technol Harbin 150040 Peoples R China

Hand-held mobile laser scanning (HMLS) can quickly and effectively obtain tree point cloud data, which has great potential to conduct forest resource survey at the plot or stand scale. To improve the estimation precision of Diameter at Breast Height (DBH) for individual trees based on HMLS data, four algorithms were compared and the ideal conditions (slice thickness, inclination, point cloud integrity) of use for each algorithm were evaluated. First, the original point cloud data were denoised, sliced, layered, and clustered based on the normalized point cloud data. Then, the tree DBH was estimated by the four algorithms under different conditions. Results showed that: (1) The optimal point cloud thickness for estimating tree DBH was 5 cm for the two-dimensional algorithms and 15 cm for the three-dimensional algorithm. (2) The root mean square error (RMSE) varied by approximately 0.5 and 0.18 cm with the change of inclination for two-dimensional algorithms and three-dimensional algorithm, respectively. (3) A reduction in point cloud integrity can affect the accuracy of all four algorithms, while the fast convex hull algorithm was the most impacted. The two-dimensional algorithms were computationally more efficient than the three-dimensional one. However, the former was less robust than the latter.

关键词： Handheld mobile laser scanning DBH Hough transform algorithm fast convex hull algorithm ransac algorithm nonlinear least-squares cylindrical-fitting algorithm

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Research on Binocular Visual System of Robotic Arm Based on Improved SURF algorithm

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IEEE SENSORS JOURNAL 2020年第20期20卷 11849-11855页

作者： Sheng, Haiyan Wei, Shimin Yu, Xiuli Tang, Ling Beijing Univ Posts & Telecommun Coll Automat Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Century Coll Beijing 100096 Peoples R China

With the increasingly extensive application of robots in industrial production, visual of the robotic arm has become an important research direction in the field of robots, which is also recognized as believed to widely considered to the most vital directions of modern high-tech development. In the robot vision system, the binocular visual system is most widely applied. When the robotic arm based on binocular vision captures the object, SURF algorithm is adopted to realize feature point extraction and matching of images. Because of the influence of the objective factors, there is false match in the process of SURF matching of feature points. In this paper, SURF algorithm is improved and reverse feature vectors matching strategy is used to initialize the matching points set. Then the ransac algorithm is adopted to select the initial matching points set in accordance of the polar geometric model to get the final good match set. The experiment results show that the improved SURF algorithm has higher registration accuracy and faster speed than SURF.

关键词： Feature extraction Cameras Robot sensing systems Service robots Visualization Machine vision Robotic arm binocular visual SURF algorithm ransac algorithm feature point matching

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Segmentation of Communication Cabinets Based on Point Cloud Coverage From LiDAR Point Cloud

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Wang, Jingru Wang, Cheng Xi, Xiaohuan Du, Meng Nie, Sheng Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The settlement and tilt of a communication base station's cabinet can harm electronic components, affecting stable operation. A 3-D model measures cabinet deformation using light detection and ranging (LiDAR) data, facilitating timely maintenance. While limited studies address cabinet segmentation, existing methods, relying on line or plane detection, face challenges with interference from objects sharing features with the cabinet. This letter proposes a cabinet point cloud segmentation method that combines cluster analysis and plane point cloud coverage (PCC). The method preprocesses the point cloud, analyzes the differences in normal vector characteristics in different points neighborhoods, segments the point cloud based on the given normal vector value, performs Euclidean clustering, and extracts the planar point cloud using the ransac algorithm to judge if it meets the characteristics of the cabinet. The proposed method is validated with nine datasets, demonstrating a 98.23% average F1-score. Results confirm the algorithm's accuracy in extracting cabinet point cloud, showcasing its versatility compared to common algorithms.

关键词： Point cloud compression Vectors Laser radar Feature extraction Clustering algorithms Data mining Classification algorithms Cabinet Euclidean clustering point cloud coverage (PCC) point cloud segmentation ransac algorithm

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Surface fitting for individual image thresholding and beyond

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IET IMAGE PROCESSING 2013年第6期7卷 596-605页

作者： Cai, Jinhai Miklavcic, Stan Univ S Australia Phen & Bioinformat Res Ctr Adelaide SA 5095 Australia Univ Adelaide Plant Genom Ctr Australian Ctr Plant Funct Genom Urrbrae SA 5064 Australia

In this study, the authors propose a novel algorithm for background-foreground segmentation. The work is motivated by the need for information about the background that is obscured by objects, in order to achieve accurate segmentation. The algorithm utilises the principle of estimating the occluded background by surface fitting. Edge detection methods are used to detect boundaries between foreground and background, identifying background points as well as foreground points. This categorisation will guarantee that most points used for surface fitting are from the same category and thus the proposed surface fitting with random sample consensus (ransac) algorithm will produce an accurate estimate of the surface. The authors algorithm has been applied to the real-world applications of segmenting plant images with inhomogeneous but smooth background and measuring the relative temperature of plants. Comparisons with experimental results show that the proposed algorithm is able to reduce significantly background inhomogeneities in infra-red images for the accurate estimation of temperature differences between background and plants, which provides important clues for fast and cheap genetic screening. The proposed algorithm is also able to overcome the intensity inhomogeneities for accurate image segmentation, particularly for plant root image segmentation with the preservation of lateral plant roots.

关键词： botany curve fitting edge detection image segmentation infrared imaging temperature measurement surface fitting image thresholding backgroundaEuro"foreground segmentation occluded background estimation edge detection boundary detection ransac algorithm temperature measurement infrared image temperature difference estimation plant root image segmentation

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Evaluation of surface damage for in-service deteriorated agricultural concrete headworks using 3D point clouds by laser scanning method

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PADDY AND WATER ENVIRONMENT 2024年第2期22卷 257-269页

作者： Shibano, Kazuma Morozova, Nadezhda Ito, Yuji Shimamoto, Yuma Tachibana, Yuki Suematsu, Kakutaro Chiyoda, Atsushi Ito, Hisaya Suzuki, Tetsuya Niigata Univ Grad Sch Sci & Technol 8050 2 ChoNishi Ku Niigata 9502181 Japan Niigata Univ Fac Agr 8050 2 ChoNishi Ku Niigata 9502181 Japan Tokyo Univ Agr & Technol Inst Agr 3-5-8 Saiwai Cho Fuchu Tokyo 1340006 Japan Nihon Suiko Consultant Co Ltd 2-50 Higashi ChoOmiya Ku Saitama Saitama 3300841 Japan

In the agricultural field, concrete headworks is the most important structure for the irrigation system. In recent years, a number of agricultural concrete infrastructures aging for a long-term period have been increasing. For maintenance and management, conventional inspection methods are time-consuming and costly, such as the electromagnetic wave method and elastic wave method. The detection of surface damage is more effective, safe and reliable than before since the laser scanning method provides detailed geometric information about the structure. The fundamental studies on point cloud data have been conducted in the civil engineering fields;nevertheless, the characteristics of point cloud in agricultural infrastructures, such as dam, headworks and canal, have not been discussed. In this study, 3D point clouds are generated for a concrete irrigation structure using the laser scanning method. The characteristics of surface damage which are quantitatively evaluated using point cloud information, geometric information and intensity parameter are investigated. The types of detected damage are efflorescence and cracks. It is investigated whether point clouds generated from a single scan or multiple scans are more effective for highly accurate detection. The characteristics of surface damage are evaluated by geometric features. The distance between the fitted plane and points is calculated by ransac algorithm and roughness parameter. The amount of efflorescence is detected by the distance between the fitted plane from ransac algorithm and points. The crack is detected by the local plane fitting method. The types of damage are characterized by the intensity parameter which is related to the color, roughness and moisture of the object. The surface damage and condition are evaluated by both geometric information and intensity parameter. These results show the unique parameters of point clouds from laser scanning methods, such as geometric features and intensity parameter,

关键词： Laser scanning method Three-dimensional point clouds Concrete headworks Surface damage ransac algorithm

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Cliff face rock slope stability analysis based on unmanned arial vehicle (UAV) photogrammetry

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GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES 2019年第4期5卷 333-344页

作者： Wang, Shuhong Ahmed, Zulkifl Hashmi, Muhammad Zaffar Wang Pengyu Northeastern Univ Sch Resource & Civil Engn Shenyang 110819 Liaoning Peoples R China COMSATS Univ Islamabad Dept Meteorol Islamabad Pakistan

Rock cliff faces are mostly avoided in rock engineering research, especially where the scale of the slope exceeds the scale of fracturing present in the rock mass. The research to understand the rock cliff face joints plays an important role in slope engineering. This paper presents a rock block identification method based on unnamed aerial vehicle photogrammetry and its computer implementation of Cliff face rock, Fort Munro Pakistan. An advance bundle adjustment method was used to build the real three-dimensional (3D) surface model of the rock mass. Later, random sample consensus algorithm was used to search the structural planes in cloud point model. A computer program, geotechnical structure and model analysis (GeoSMA-3D) was developed to solve the engineering problems and to verify the example. The program confirmed the efficiency of the theory in dealing with complex surface modelling and cliff face rocky slope stability analysis. The approach presented can be applied as a general guiding design principle for cliff face rock slope.

关键词： Rock cliff face Block theory Bundle adjustment method ransac algorithm UAV photogrammetry

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Robust and accurate iris segmentation in very noisy iris images

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IMAGE AND VISION COMPUTING 2010年第2期28卷 246-253页

作者： Li, Peihua Liu, Xiaomin Xiao, Lijuan Song, Qi Heilongjiang Univ Sch Comp Sci & Technol Harbin 150080 Hei Long Jiang Peoples R China

Iris segmentation plays an important role in an accurate iris recognition system. In less constrained environments where iris images are captured at-a-distance and on-the-move, iris segmentation becomes much more difficult due to the effects of significant variation of eye position and size, eyebrows, eyelashes, glasses and contact lenses, and hair, together with illumination changes and varying focus condition. This paper contributes to robust and accurate iris segmentation in very noisy images. Our main contributions are as follows: (1) we propose a limbic boundary localization algorithm that combines K-Means clustering based on the gray-level co-occurrence histogram and an improved Hough transform, and, in possible failures, a complementary method that uses skin information;the best localization between this and the former is selected. (2) An upper eyelid detection approach is presented, which combines a parabolic integro-differential operator and a ransac (RANdom SAmple Consensus)-like technique that utilizes edgels detected by a one-dimensional edge detector. (3) A segmentation approach is presented that exploits various techniques and different image information, following the idea of focus of attention, which progressively detects the eye, localizes the limbic and then pupillary boundaries, locates the eyelids and removes the specular highlight. The proposed method was evaluated in the UBIRIS.v2 testing database by the NICE.I organizing committee. We were ranked #4 among all participants according to the evaluation results. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Iris segmentation Integro-differential operator K-Means clustering ransac algorithm

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A new pose estimation method for non-cooperative spacecraft based on point cloud

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International Journal of Intelligent Computing and Cybernetics 2019年第1期12卷 23-41页

作者： Zhiming Chen Lei Li Yunhua Wu Bing Hua Kang Niu College of Astronautics Nanjing University of Aeronautics and AstronauticsNanjingChina Shanghai Institute of Mechanical and Electrical Engineering ShanghaiChina

Purpose–On-orbitservice technologyis one of the key technologies of space manipulation activities such as spacecraft life extension,fault spacecraft capture,on-orbit debris removal and so *** is known that the failure satellites,space debris and enemy spacecrafts in space are almost all non-cooperative *** accurate pose estimation is critical to spatial operations,but also a recognized technical difficulty because of the undefined prior information of non-cooperative *** the rapid development of laser radar,the application of laser scanning equipment is increasing in the measurement of non-cooperative *** is necessary to research a new pose estimation method for non-cooperative targets based on 3D point *** paper aims to discuss these ***/methodology/approach–In this paper,a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose(position and attitude)of the spatial non-cooperative ***,we need to preprocess the obtained point cloud to reduce noise and improve the quality of ***,according to the features of the satellite,a recognition system used for non-cooperative measurement is *** components which are common in the configuration of satellite are chosen as the recognized ***,based on the identified object,the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose ***–The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching *** recognition of components on non-cooperative spacecraft directly contributes to the space docking,on-orbit capture and relative *** limitations/implications–Limited to the measurement distance of the laser radar,this paper considers the pose estimation for non-cooperative spacecraft in the close *** implications–

关键词： Point cloud Pose estimation ICP algorithm Non-cooperative spacecraft ransac algorithm

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Sensor Data Fusion algorithm for Indoor Environment Mapping Using Low-Cost Sensors

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2013年第3期24卷 199-211页

作者： Buonocore, Luciano Nascimento Junior, Cairo Lucio Neto, Areolino de Almeida Univ Fed Maranhao Dept Engn Eletricidade BR-65085581 Sao Luis MA Brazil Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil

This article is concerned with 2D indoor environment mapping produced by a sensor data fusion algorithm. Ameasurement subsystem assembled on a mobile robot uses its sensors to gather data from its surroundings. In this article, an algorithm is proposed and evaluated to fuse data acquired by the following low-cost sensors: a visual sensor (a wireless webcam with a laser pointer) and three range finder sensors (two infrared units and a sonar transducer). The main steps of the proposed solution are: (a) at each accurate robot pose an occupancy grid (OG) probabilistic map is generated for each type of sensor, (b) the three OG maps are merged using competitive fusion and the ransac algorithm is employed to extract line segments, (c) the line segments are further processed using competitive and/or complementary fusion resulting in a feature-based map for each robot pose. The previous steps are repeated until all robots pose are computed. The feature-based maps for all robot poses are then merged using competitive fusion, and a final precise OG map of the environment is generated. The proposed algorithm was evaluated using data gathered by the SLAMVITA robot sensors in two small test environments. The experimental results have shown that the proposed algorithm was able to build precise maps of the test environments (error less than 1.5%).

关键词： Sensor data fusion Visual sensor Low-cost sensors Probabilistic mapping ransac algorithm

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