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Online Triangular Constraint Calibration for LiDAR and Cameras in Open-Pit Mines

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IEEE Transactions on Field Robotics

IEEE Transactions on Field robotics 2024年 2卷 131-146页

作者： Yuchen Li Luxi Li Siyu Teng Zhenshan Bing Alois Christian Knoll Zhe Xuanyuan Long Chen Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China Faculty of Science Hong Kong Baptist University Hong Kong SAR China Research and Development Center WAYTOUS Inc. Beijing China Department of Information Technische Universität München Munich Germany State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Artificial Intelligence Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China

Extrinsic calibration between a LiDAR and a camera is a crucial task for intelligent vehicles and mobile robots. However, most of the current extrinsic calibration methods are offline and target-based, making it hard to achieve real-time calibration depending on the characteristics of the environments. Although a few online calibration strategies can solve the above drawbacks, they still rely on a large amount of labeled data and struggle to achieve satisfactory performance in extrinsic calibration in unstructured scenarios where valid targets are scarce. In this work, we propose an effective online calibration strategy for mining scenarios, one of the typical unstructured scenarios. Based on the analysis of mining area data, we adopt an approach that integrates image and point cloud segmentation to accomplish the entire calibration process. An initialization method supporting a single rigid target is proposed, effectively addressing the initialization failure caused by the scarcity of targets in mining areas. Incorporating the segmentation results from the right camera to establish a left-right camera-LiDAR triangular constraint effectively reduces translation errors. Temporal data with confidence scores are then used to update and optimize the extrinsic parameters. Our online extrinsic calibration method has been validated on mining scenarios data and has achieved less than 0.2 m in data collected from an SUV and 0.3 m from trucks in translation errors within 10 frames. Besides, we provide a detailed discussion on the challenges of calibration in mining areas and the limitations of the proposed method.

关键词： Calibration Image segmentation Cameras Sensors Point cloud compression Laser radar Translation Image edge detection Accuracy Mining industry

A social power game for the concatenated Friedkin-Johnsen model

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A social power game for the concatenated Friedkin-Johnsen mo...

IEEE Conference on Decision and Control

作者： Lingfei Wang Guanpu Chen Yiguang Hong Guodong Shi Claudio Altafini Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences and also with the University of Chinese Academy of Sciences Beijing China JD Explore Academy Beijing China Department of Control Science and Engineering Tongji University Shanghai China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Sydney Department of Electrical Engineering Division of Automatic Control Linköping University Linköping Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

If a concatenated Friedkin-Johnsen model is used to describe the evolution of the opinions of stubborn agents in a sequence of discussion events, then the social power achieved by the agents at the end of the discussions depends from the stubbornness coefficients adopted by the agents through the sequence of events. In this paper we assume that the agents are free to choose their stubbornness profiles, and ask ourselves what strategy should an agent follow in order to maximize its social power. Formulating the problem as a strategic game, we show that choosing the highest possible values of stubbornness in the early discussions leads to the highest possible social power.

关键词： Games

Multiscale regression on unknown manifolds

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arXiv 2021年

作者： Liao, Wenjing Maggioni, Mauro Vigogna, Stefano School of Mathematics Georgia Institute of Technology AtlantaGA30313 United States Department of Mathematics Department of Applied Mathematics and Statistics Mathematical Institute of Data Science Johns Hopkins University BaltimoreMD21218 United States MaLGa Center Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Genova16145 Italy

We consider the regression problem of estimating functions on RD but supported on a d-dimensional manifold M ⊂ RD with d D. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on M at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension d, instead of an unknown manifold embedded in RD. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in D and exponential in d. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees. Copyright © 2021, The Authors. All rights reserved.

关键词： Regression analysis

Temporal networks provide a unifying understanding of the evolution of cooperation

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arXiv 2023年

作者： Li, Aming Meng, Yao Zhou, Lei Masuda, Naoki Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Mathematics State University of New York at Buffalo BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260 United States

Understanding the evolution of cooperation in structured populations represented by networks is a problem of long research interest, and a most fundamental and widespread property of social networks related to cooperation phenomena is that the node’s degree (i.e., number of edges connected to the node) is heterogeneously distributed. Previous results indicate that static heterogeneous (i.e., degree-heterogeneous) networks promote cooperation in stationarity compared to static regular (i.e., degree-homogeneous) networks if equilibrium dynamics starting from many cooperators and defectors is employed. However, the above conclusion reverses if we employ non-equilibrium stochastic processes to measure the fixation probability for cooperation, i.e., the probability that a single cooperator successfully invades a population. Here we resolve this conundrum by analyzing the fixation of cooperation on temporal (i.e., time-varying) networks. We theoretically prove and numerically confirm that on both synthetic and empirical networks, contrary to the case of static networks, temporal heterogeneous networks can promote cooperation more than temporal regular networks in terms of the fixation probability of cooperation. Given that the same conclusion is known for the equilibrium fraction of cooperators on temporal networks, the present results provide a unified understanding of the effect of temporal degree heterogeneity on promoting cooperation across two main analytical frameworks, i.e., equilibrium and non-equilibrium ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Heterogeneous networks

Semiconductor Manufacturing Industry: Assessment, Challenges, and Future Trends 2

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Semiconductor Manufacturing Industry: Assessment, Challenges...

2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Musa, Yasmin Tantawi, Khalid Mikhail, Maged Ma, Jeffrey Alharthi, Dalal Flatt, Larry Potter, Lyn Motlow State Community College Department of Career Readiness SmyrnaTN United States Department of Engineering Management and Technology University of Tennessee at Chattanooga TN United States Purdue University Northwest Hammond Department of Engineering Technology IN United States Saint Louis University Saint Louis Department of Mechanical Engineering MO United States College of Applied Science and Technology University of Arizona Tuscan AZ United States Robotics and Automation Center Motlow State Community College McMinnvilleTN United States Chattanooga State Community College Chattanooga State Community College Dept. of Engineering Systems Tech. ChattanoogaTN United States

ISBN: (纸本)9798331506995

In this work we evaluate the state of the semiconductor manufacturing industry and its challenges and trends. Future trends in the industry are analyzed from three perspectives: the evolution of Industry 4.0, the advances in semiconductor materials, and the impact of the Covid-19 Pandemic. The semiconductor manufacturing industry witnessed an acute decline in the United States and other regions in the two decades prior to the CoVid-19 pandemic. The decline was only uncovered after the chip shortage of 2021 that resulted from the severe supply chain disruption. Trends in the industry were analyzed from three perspectives: Industry 4.0, advances in materials, and the Post-pandemic era. As a result of the evolution of the fourth generation of industry (Industry 4.0), trends in semiconductor manufacturing include robotization, which caused the industry to become the largest market for industrial robotics since 2020, and an all-time peak globalization. The semiconductor industry is a very globalized industry with corporates from different parts of the world taking part in the production of the final product. Although some materials such as carbon and Gallium Nitride show promising trends to replace silicon as the material of choice. It will likely not be before two or three decades when a semiconductor material will be able to replace silicon. Challenges for the industry include the shortage of the trained-workforce, and the added inter-country restrictions that may hinder the globalization of the industry. © 2025 IEEE.

关键词： Semiconductor alloys

Imitation dynamics on networks with incomplete information

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arXiv 2023年

作者： Wang, Xiaochen Zhou, Lei McAvoy, Alex Li, Aming Center for Systems and Control College of Engineering Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China

Imitation is an important learning heuristic in animal and human societies. Previous explorations report that the fate of individuals with cooperative strategies is sensitive to the protocol of imitation, leading to a conundrum about how different styles of imitation quantitatively impact the evolution of cooperation. Here, we take a different perspective on the personal and external social information required by imitation. We develop a general model of imitation dynamics with incomplete information in networked systems, which unifies classical update rules including the death-birth and pairwise-comparison rule on complex networks. Under pairwise interactions, we find that collective cooperation is most promoted if individuals neglect personal information. If personal information is considered, cooperators evolve more readily with more external information. Intriguingly, when interactions take place in groups on networks with low degrees of clustering, using more personal and less external information better facilitates cooperation. Our unifying perspective uncovers intuition by examining the rate and range of competition induced by different information situations. © 2023, CC BY.

关键词： Complex networks

Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots

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SSRN

SSRN 2024年

作者： Wu, Zhiguang Lu, Liang Bai, Shuang Shi, Jiaqi Zhang, Zhuotao Pan, Yunlu Wang, Wei Wu, Wenfeng Sun, Shoubin Li, Xu Hou, Gang Huang, Tianyun Jia, Yuxin Zuo, Kaiwen Granovsky, Alexander Wu, He Nikolai, Perov School of Medicine and Health Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China The First Affiliated Hospital of Harbin Medical University Harbin China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and Multimorbidity National Clinical Research Center for Respiratory Diseases Institute of Respiratory Medicine Chinese Academy of Medical Sciences Department of Pulmonary and Critical Care Medicine Center of Respiratory Medicine China-Japan Friendship Hospital Beijing China College of Science Sichuan Agricultural University Chengdu China The Fourth Affiliated Hospital of Harbin Medical University Harbin China Department of Ophthalmology The Second Hospital Jilin University Changchun China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Mathematics Imperial College London United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Magnetism Department Faculty of Physics Lomonosov Moscow State University Moscow Russia Harbin Medical University China

Learning from natural bacteria flagellum, we demonstrate a magnetic polymer multilayer conical microrobot that bestow the controllable propulsion upon external rotating magnetic field with uniform intensity. The magnetic microrobots were constructed by template-assisted Layer-by-Layer technique technique and subsequent functionalization of magnetic particle onto the big opening of the microrobots. Geometric variables of the polymer microrobots, such as the diameter and wall thickness, can be controlled by selection of porous template and layers of assembly. The microrobots perform controllable propulsion through the manipulation of magnetic field. The comparative analysis of the movement behavior reveals that the deformation of microrobots may attributed to the propulsion upon rotating magnetic field, which is similar as that of natural bacteria. The influence of actuation frequencies and frequencies on the velocity of the microrobots is studied. Such use of polymer polymer multilayer tubular micro/nanoarchitectures results in significant savings in the processing costs and provides a simple and cost-effective fabrication route for the mass production of magnetic polymer microrobots. © 2024, The Authors. All rights reserved.

关键词： Bacteria

Physics Informed Shallow Machine Learning for Wind Speed Prediction

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arXiv 2022年

作者： Lagomarsino-Oneto, Daniele Meanti, Giacomo Pagliana, Nicolò Verri, Alessandro Mazzino, Andrea Rosasco, Lorenzo Seminara, Agnese Department of Civil Chemical and Environmental Engineering University of Genoa Genoa Italy MaLGa Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy MaLGa Department of Mathematics University of Genoa Genoa Italy National Institute of Nuclear Physics Genoa Section Genoa Italy Center for Brains Minds and Machines Mit CambridgeMA United States Italian Institute of Technology Genoa Italy

The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative datadriven approach based on supervised learning. We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in two central and north west regions of Italy (Abruzzo and Liguria). We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon). Using data from a single location and time horizon we compare systematically several algorithms where we vary the input/output variables, the memory of the input and the linear vs non-linear learning model. We then compare performance of the best algorithms across all locations and forecasting horizons. We find that the optimal design as well as its performance vary with the location. We demonstrate that the presence of a reproducible diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms and show that, when the model is accurately designed, shallow algorithms are competitive with more complex deep architectures. © 2022, CC BY-NC-SA.

关键词： Wind

Look closer to segment better: Boundary patch refinement for instance segmentation

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arXiv 2021年

作者： Tang, Chufeng Chen, Hang Li, Xiao Li, Jianmin Zhang, Zhaoxiang Hu, Xiaolin State Key Laboratory of Intelligent Technology and Systems THU-Bosch JCML Center BNRist Institute for AI Department of Computer Science and Technology Tsinghua University China Institute of Automation CAS University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HKISI CAS China

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the "PolyTransform + SegFix" baseline, we reached 1st place on the Cityscapes leaderboard. Code is available at https://***/tinyalpha/BPR. Copyright © 2021, The Authors. All rights reserved.

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