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APPLICATION OF LIQUID CRYSTAL LIGHT VALVE IN robot vision SYSTEM

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APPLICATION OF LIQUID CRYSTAL LIGHT VALVE IN ROBOT VISION SY...

Learning a Deep Metric:A Lightweight Relation Network for Loop Closure in Complex Industrial Scenarios

2006年中国机械工程学会年会暨中国工程院机械与运载工程学部首届年会

作者： WANG Tao, LI Dongmei, TAO Chunkuang College of Mathematics and Physics, Chongqing University, Chongqing, 400044, China

The robot vision system is designed basing on optical information processing 4f system and photoelectricity information technology. It is applied successfully in the Fourier transform and wavelet tran

关键词： Liquid crystal light valve robot vision optical wavelet transform.

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Chinese Journal of Electronics 2021年第1期30卷 45-54页

作者： JIN Sheng CHEN Liang GAO Yu SHEN Changqing SUN Rongchuan School of Mechanical and Electric Engineering Soochow University School of Rail Transportation Soochow University

The similarity metric in Loop closure detection(LCD) is still considered in an old fashioned way, i.e. to pre-define a fixed distance function, leading to a limited performance. This paper proposes a general framework named LRN-LCD, i.e. a Lightweight relation network for LCD, which combines the feature extraction module and similarity metric module into a simple and lightweight network. The LRN-LCD, an end-to-end framework, can learn a non-linear deep similarity metric to detect loop closures from different scenes. Moreover, the LRN-LCD supports image sequences as input to speed up the similarity metric in real-time applications. Extensive experiments on several open datasets illustrate that LRN-LCD is more robust to strong condition variations and viewpoint variations than the mainstream methods.

关键词： lightweight relation network image sequences similarity metric feature extraction module SLAM (robots) loop closure detection robot vision nonlinear deep similarity loop closures fixed distance function learning (artificial intelligence) LRN-LCD framework feature extraction deep metric

On-orbit real-time robust cooperative target identification in complex background

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Chinese Journal of Aeronautics 2015年第5期28卷 1451-1463页

作者： Wen Zhuoman Wang Yanjie Arjan Kuijper Di Nan Luo Jun Zhang Lei Jin Minghe Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences University of the Chinese Academy of Sciences Fraunhofer Institute for Computer Graphics Research Technische Universitt Darmstadt State Key Laboratory of robotics and System Harbin Institute of Technology

Cooperative target identification is the prerequisite for the relative position and orientation measurement between the space robot arm and the to-be-arrested object. We propose an on- orbit real-time robust algorithm for cooperative target identification in complex background using the features of circle and lines. It first extracts only the interested edges in the target image using an adaptive threshold and refines them to about single-pixel-width with improved non-maximum suppression. Adapting a novel tracking approach, edge segments changing smoothly in tangential directions are obtained. With a small amount of calculation, large numbers of invalid edges are removed. From the few remained edges, valid circular arcs are extracted and reassembled to obtain circles according to a reliable criterion. Finally, the target is identified if there are certain numbers of straight lines whose relative positions with the circle match the known target pattern. Experiments demonstrate that the proposed algorithm accurately identifies the cooperative target within the range of 0.3 1.5 m under complex background at the speed of 8 frames per second, regardless of lighting condition and target attitude. The proposed algorithm is very suitable for real-time visual measurement of space robot arm because of its robustness and small memory requirement.

关键词： Circle detection Edge extraction Edge tracking Line detection robot vision Space robot arm

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

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International Journal of Automation and computing 2017年第2期14卷 147-158页

作者： Ying Xie Xiang-Dong Yang Zhi Liu Shu-Nan Ren Ken Chen Department of Mechanical Engineering Tsinghua University Beijing 100084 China Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Beijing 100084 China State Key Laboratory of Tribology Beijing 100084 China

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%.

关键词： robot vision visual localization 3D object localization model based pose estimation distance function of projectedfeatures nonlinear least squares random sample consensus (RANSAC).

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Scene images and text information‐based object location of robot grasping

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IET Cyber-Systems and robotics 2022年第2期4卷 116-130页

作者： Zhichao Liu Kaixuan Ding Qingyang Xu Yong Song Xianfeng Yuan Yibin Li School of Mechanical Electrical&Information EngineeringShandong UniversityWeihaiChina School of Control Science and Engineering Shandong UniversityJinanChina

With the rapid development of artificial intelligence(AI),the application of this technology in the medical field is becoming increasingly extensive,along with a gradual increase in the amount of intelligent equipment in *** robots can save human resources and replace nursing staff to achieve some *** view of the phenomenon of mobile service robots'grabbing and distribution of patients'drugs in hospitals,a real‐time object detection and positioning system based on image and text information is proposed,which realizes the precise positioning and tracking of the grabbing objects and completes the grasping of a specific object(medicine bottle).The lightweight object detection model NanoDet is used to learn the features of the grasping objects and the object category,and bounding boxes are ***,the images in the bounding boxes are enhanced to overcome unfavourable factors,such as a small object *** text detection and recognition model PP‐OCR is used to detect and recognise the enhanced images and extract the text *** object information provided by the two models is fused,and the text recognition result is matched with the object detection box to achieve the precise posi-tioning of the grasping *** kernel correlation filter(KCF)tracking algorithm is introduced to achieve real‐time tracking of specific objects to precisely control the robot's *** deep learning models adopt lightweight networks to facilitate direct *** experiments show that the proposed robot grasping detection system has high reliability,accuracy and real‐time performance.

关键词： dexterous manipulators neural network‐based,robot control robot vision service robots

ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

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IET Cyber-Systems and robotics 2024年第2期6卷 97-108页

作者： Changjian Jiang Zeyu Wan Ruilan Gao Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province HangzhouChina

The colour-enhanced point cloud map is increasingly being employed in fields such as robotics,3D reconstruction and virtual *** authors propose ER-Mapping(Extrinsic Robust coloured Mapping system using residual evaluation and selection).ER-Mapping consists of two components:the simultaneous localisation and mapping(SLAM)subsystem and the colouring *** SLAM subsystem reconstructs the geometric structure,where it employs a dynamic threshold-based residual selection in LiDAR-inertial odometry to improve mapping *** the other hand,the col-ouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods,eliminating the need for strict hardware time synchronisation and highly accurate extrinsic *** were conducted in both indoor and outdoor *** results demonstrate that our system can enhance accuracy,reduce computational costs and achieve extrinsic robustness.

关键词： robot vision sensor fusion simultaneous localisation and mapping(robots)

Monitoring a Wide Manufacture Field Automatically by Multiple Sensors

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自动化学报 2006年第6期32卷 956-967页

作者： LU Jian HAMAJIMA Kyoko JIANG Wei National Institute of Industrial Safety 1-4-6 Umezono Kiyose Tokyo 204 - 0024 Japan Japan Science and Technology Agency 1-8 Honcho 4 Kawaguchi City Saitama 332 - 0012 Japan

This research is dedicated to develop a safety measurement for human-machine cooperative system, in which the machine region and the human region cannot be separated due to the overlap and the movement both from human and from machines. Our proposal here is to automatically monitor the moving objects by image sensing/recognition method, such that the machine system can get enough information about the environment situation and about the production progress at any time, and therefore the machines can accordingly take some corresponding actions automatically to avoid hazard. For this purpose, two types of monitor systems are proposed. The first type is based on the omni directional vision sensor, and the second is based on the stereo vision sensor. Each type may be used alone or together with another type, depending on the safety system's requirements and the specific situation of the manufacture field to be monitored. In this paper, the description about these two types are given, and as for the special application of these image sensors into safety control, the construction of a hierarchy safety system is proposed.

关键词： Sensor network robot vision safety control stereo vision omni-direction vision

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A Compact 3D vision System

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光电子·激光 1997年第1期8卷 51-55页

作者： Yun Zhisheng Liu Jifang He Zhengquan Li Yulin (Xi’an institute of Optics & Precision Mechanics, Academia Sinica, Xi’an 710068) 中科院西安光学精密机械所陕西西安

A compact 3D vision system is presented. The system is based essentially on synchronized scanners which greatly reduces the optical head size, so the shadow effects are reduced proportionally. By using the reflective prism, the arrangement is easy to adjust. With the use of Sheimpflug condition in the observation device, a large depth of field, and at the same time,high resolution are obtained.

关键词： robot vision range measurement optical sensor

Deep Learning-Based 3D Instance and Semantic Segmentation: A Review

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Journal on Artificial Intelligence 2022年第2期4卷 99-114页

作者： Siddiqui Muhammad Yasir Hyunsik Ahn Department of robot System Engineering Tongmyong UniversityBusan48520Korea School of Artificial Intelligence Tongmyong UniversityBusan48520Korea

The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D *** is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent *** research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and *** learning has been successfully used to a spectrum of 2D vision domains as a prevailing ***,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial *** study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D *** these approaches’benefits,draw backs,and design mechanisms are studied and *** study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.

关键词： Artificial intelligence computer vision robot vision 3D instance segmentation 3D semantic segmentation 3D data deep learning point cloud mesh voxel RGB-D segmentation