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共 5312 条记录，以下是771-780 订阅

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Fast Feature Matching in Visual-Inertial SLAM

Fast Feature Matching in Visual-Inertial SLAM

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Lin Feng Xinyi Qu Xuetong Ye Kang Wang Xueyuan Li 92228 Troops Inner Mongolia First Machinery Group Co. Ltd. School of Mechanical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665476881

Feature matching is an important step for SLAM with high real-time requirements. Currently, most feature matching methods only rely on visual information, even in visual-inertial SLAM. In this paper, we propose an efficient feature matching method for point and line features by fusing IMU information with visual information. the key ideas are utilizing IMU pre-integration to estimate the relative pose change of two consecutive frames and narrow the feature search area to accelerate feature matching. Experiment results show that our method shortens the feature matching time significantly compared with other feature matching methods while ensuring high matching accuracy.

关键词： Visualization Simultaneous localization and mapping automation Real-time systems Robots

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CNN-based Joint State Estimation During Robotic Interaction with Articulated Objects

CNN-based Joint State Estimation During Robotic Interaction ...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Kamil Młodzikowski Dominik Belter Institute of Robotics and Machine Intelligence Poznan University of Technology Poznan Poland

ISBN: (纸本)9781665476881

In this paper, we investigate the problem of state estimation of rotational articulated objects during robotic interaction. We estimate the position of a joint axis and the current rotation of an object from a pair of RGB-D images registered by the depth camera mounted on the robot. However, the camera mounted on the robot has a limited view due to occlusions of the robot's arm. Moreover, some configurations of objects are difficult to register by typical RGB-D sensors. thus, the model-based methods fail in these cases. To deal with this problem, we propose a CNN-based architecture that gradually estimates the parameters and the state of the rotational joint. To meet real-time requirements on the real robot, we propose a fast inference on 2D images without directly operating on the 3D model of the object. the proposed method is trained and verified on the RBO dataset that contains RGB-D sequences of manipulated articulated objects.

关键词： Solid modeling three-dimensional displays Robot vision systems Neural networks Cameras Real-time systems Sensors

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2018 15th international conference on control, automation, robotics and vision, ICARCV 2018

2018 15th International Conference on Control, Automation, R...

引用

15th international conference on control, automation, robotics and vision, ICARCV 2018

ISBN: (纸本)9781538695821

the proceedings contain 339 papers. the topics discussed include: design of a data-driven two-degree-of-freedom control system considering robustness;a novel structure of convolutional layers with a higher performance-complexity ratio for semantic segmentation;center of mass based walking pattern generator with gravity compensation for walking control on bioloid humanoid robot;digital twins: assisting and supporting cooperation in human-robot teams;fusing laser scanner and stereo camera in evidential grid maps;mixed-integer representations for mission constraints in a multi-agent team;integrated production planning and preventive maintenance model with non-fixed lead time;comapping: multi-robot sharing and generation of 3D-maps applied to rural and urban scenarios;and efficient trajectory optimization for robot motion planning.

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A Machine Learning Approach to Minimization of the Sim-To-Real Gap via Precise Dynamics Modeling of a Fast Moving Robot

A Machine Learning Approach to Minimization of the Sim-To-Re...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Alexander Kanwischer Oliver Urbann Automation and Embedded Systems Fraunhofer IML Dortmund Germany

ISBN: (纸本)9781665476881

How well simulation results can be transferred to the real world depends to a large extent on the sim-to-real gap that therefore should be as small as possible. In this work, this gap is reduced exemplarily for a robot with an omni-directional drive, which is challenging to simulate, utilizing machine learning methods. For this purpose, a motion capture system is first used to record a suitable data set of the robot's movements. then, a model based on physical principles and observations is designed manually, which includes some unknown parameters that are learned based on the training dataset. Since the model is not differentiable, the evolutionary algorithms NSGA-II and -III are applied. Finally, by the presented approach, a significant reduction of the sim-to-real gap can be observed even at higher velocities above 2 m/s. the ablation study also shows that the elements beyond normal simulations, such as the engine simulation, and the machine learning approach, are essential for success.

关键词： Training Heuristic algorithms Simulation Neural networks Wheels Training data Machine learning

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C-TM: Topo-metric Mapping and Localization based on Place Categorization and Place Recognition for a Delivery Robot on Footpath

C-TM: Topo-metric Mapping and Localization based on Place Ca...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Timothy Chia Jun Zhang Heshan Li Guohao Peng Mingxing Wen Dawei Kee P.G.C.N. Senarathne Continental-NTU Corporate Lab Nanyang Technological University Singapore Continental Automotive Singapore Pte Ltd. Singapore

ISBN: (纸本)9781665476881

In this work, C-TM is presented: a method to build a topo-metric map for delivery robot navigation in largescale city environments. this system automatically generates a compact map by only saving expensive LIDAR information at key locations. these locations form the nodes of a topological map. Nodes are identified using a Place-Categorization (PC) neural network which output the place category from RGB cameras. Inside nodes, we generate and save high quality LIDAR submaps. Global localization within the map is done with a Visual-Place-Recognition (VPR) neural network. the topo-metric map can be used for navigation on footpath. We deploy C-TM on a four-wheeled autonomous delivery robot and test the effectiveness in two environments, both day and night.

关键词： Location awareness Laser radar automation Navigation Neural networks Urban areas Robot vision systems

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MATLAB Toolbox for fractional-variable-order closed loop systems with a digital controller

MATLAB Toolbox for fractional-variable-order closed loop sys...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Piotr Oziablo Dorota Mozyrska Mariusz Matusiak Faculty of Computer Science Bialystok University of Technology Wiejska 45A Poland Faculty of Electrical Electronics Computer Science and Control Engineering Lodz University of Technology Lodz Poland

ISBN: (纸本)9781665476881

In the paper, there is presented a MATLAB Toolbox that allows simulation and parameter tuning of closed-loop systems with fractional-variable-order digital PID (FVOPID) controllers. the proposed toolbox provides MATLAB Simulink blocks of FVOPID based on two different implementations of Grünwald-Letnikov fractional-variable-order operator. An additional part of the toolbox is an application with a GUI interface which primary purpose is to simulate and find the optimal tuning parameters of FVOPID for a given closed-loop system. the tuning process, in this case, can be performed employing different optimization methods set to minimize an objective function according to the selected integral criterion. the paper describes the internal architecture (design) of the toolbox and its functionalities.

关键词： Software packages Optimization methods Programming Linear programming Calculus Closed loop systems Tuning

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A Novel Bottom-Up Semi-Supervised Learning Framework for Salient Object Detection 11

A Novel Bottom-Up Semi-Supervised Learning Framework for Sal...

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11th IEEE Annual international conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2021

作者： Pang, Yu Wu, Hao Wu, Yunhe Wu, Chengdong Zhou, Wei Yu, Xiaosheng Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China Australian Centre for Field Robotics University of Sydney Sydney Australia College of Information Science and Engineering Northeastern University Shenyang110819 China College of Computer Science Shenyang Aerospace University Shenyang110136 China

ISBN: (纸本)9781665425278

Salient object detection, which aims at locating the most important object in a scene (or image), has been extensively studied in various tasks, such as robot vision. In this paper, we present an effective salient object detection framework based on a novel bottom-up semi-supervised learning algorithm, which obviously outperforms the existing works in complex scenes. Given an input image, it is firstly segmented into a fixed number of non-overlapping image patches as basic units. A novel segmentation-based sampling method is developed to select a subset of all image patches as training samples. then, all samples are divided into labeled and unlabeled groups based on multiple prior cues. the labels of all the unlabeled data are inferred by a novel label propagation mechanism. As a result, a complete training set can be obtained and used to train a classifier to classify all image patches into salient object and background. In addition, we also use neighbor-constraint smoothness function to further boost the saliency map. We compare the proposed method with the state-of-the-art approaches on two datasets. Experimental results demonstrate the effectiveness and superiority of the proposed method. © 2021 IEEE.

关键词： Learning algorithms

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Fast and Accurate 3D Eye-To-hand Calibration for Large-Scale Scene based on HALCON 11

Fast and Accurate 3D Eye-To-hand Calibration for Large-Scale...

引用

11th IEEE Annual international conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2021

作者： Wang, Geng Quan, Wanlong Li, Yaonan Fang, Siwen Chen, Heping Xi, Ning Shenyang110016 China Shenzhen Academy of Robotics Shenzhen518057 China

ISBN: (纸本)9781665425278

the premise of robot executing vision-based tasks is to establish the coordination between robot eye (structured light 3D sensor) and a hand (end effector). However, it is inefficient to perform the 3D eye-To-hand calibration for large-scale scene due to large amounts of point clouds data contained in the scene. this paper describes a fast and accurate 3D eye-To-hand calibration method for large-scale scene. A common tee pipe is used as calibration object. the method presented here comprises the following steps: Firstly, voxel grid filtering method is used for reducing the number of point clouds data. Secondly, employing euclidean cluster extraction method, the point clouds data of the calibration object can be extracted from the large-scale scene. thirdly, the pose of the calibration object is acquired by surface-based 3D matching. Finally, the 3D eye-To-hand calibration is performed, and robot grasping experiments are carried out on ABB YuMi platform to validate the accuracy of calibration results. the experiments demonstrate that our method can achieve high calibration accuracy and significantly improve the efficiency of calibration. © 2021 IEEE.

关键词： Calibration

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A Simple Confidence-Supervised Model for High-Resolution Defect Recognition

A Simple Confidence-Supervised Model for High-Resolution Def...

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robotics, control and automation (ICRCA), international conference on

作者： Hui-Yue Yang Hui Chen Fan Zhang Xiang Tian Luning Zhu Guiguang Ding Research Center for Information Science and Technology School of Software Tsinghua University and Beijing National Tsinghua University Zhuoxi Institute of Brain and Intelligence Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China Group Corporation The 21th Research Institute of China Electronics Technology Shanghai China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

this paper addresses the critical role of chip manufacturing in advancing the high-tech industry. It emphasizes the need for quality control in the delicate and complex chip manufacturing process, particularly in the detection of surface defects on chip package images. Such detection not only ensures chip quality, but also increases production efficiency and reduces costs. this study explores the application of deep learning for chip defect detection, focusing on high-resolution images with small defect areas. the proposed classification algorithm, which is based on model confidence, consists of two key components: Single-Scale Defect Recognition (SSDR) and Cross-Scale Model Fusion (CSMF). First, the chip image is pre-processed and partitioned into multi-scale clippings. then, a ResNet-18 model is trained on these different scale clippings. Confidence thresholds for positive and negative cases are set to improve the accuracy. the final defect classification decision is made by considering the results of the multi-scale clippings together. Experimental results show that both methods significantly improve the classification accuracy compared to the direct evaluation of the clippings. In particular, CSMF outperforms SSDR by 16.78%.

关键词： Deep learning Industries Accuracy Manufacturing processes Production Quality control Manufacturing

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Integrating Augmented Reality within Digital Twins for Smart Robotic Manufacturing Systems

Integrating Augmented Reality within Digital Twins for Smart...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： Toméš Jochman Václav Voltr Václav Kubáček Ondřej Švec Pavel Burget Václav Hlaváč Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

this paper explores the integration of augmented reality into robotic manufacturing systems, emphasizing the enhancement of connectivity, real-time data processing, and interactive visual interfaces. Utilizing Hololens 2 headsets equipped with OPC UA clients, the system establishes a direct interface between operators and the digital and physical components of the manufacturing environment. the architecture employs augmented reality to facilitate sophisticated operational control and visualization, ranging from robotic additive manufacturing to complex tasks like robotic sanding and screwing with integrated camera systems. this integration showcases significant advancements in manufacturing processes, allowing operators to engage interactively with the systems, optimize workflows, and improve overall precision and efficiency in a smart factory setting.

关键词： Visualization Manufacturing processes Robot vision systems Cameras Real-time systems User experience Manufacturing Workstations Robots Augmented reality

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