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2019中国智能自动化大会(CIAC 2019)

作者： Ran Xiao Hao Du Chaowen Xu Wei Wang Institute of Applied Research Intelligent Science and Technology Jiangsu and Chinese Academy of Sciences Changzhou Research and Industrialization Center for Advanced Manufacturing Technology Chinese Academy of Sciences School of Automation Nanjing University of Information Science and Technology

An efficient real-time autonomous drone navigation and path planning system is proposed by using an RGB-D sensor in the GPS-denied indoor ***,the RGB images and their corresponding depth images collected by the sensor were used for real-time pose estimation and dense point cloud map reconstruction based on ORB-SLAM(Simultaneous Localization And Mapping system based on the ORiented BRIEF feature).With the attitude angle data from the Inertial Measurement Unit(IMU) used in the pose initialization,the SLAM system can provide a more accurate pose information,and therefore a better 3D map when the drone flies with a high *** the second phase,a simple and efficient pathfinding program was *** this procedure,the point cloud map data would be translated to a map based on the octree structure,which contains the occupied and free *** size of occupied nodes in the octree map is three times smaller than that of free nodes,which ensures more free area for the drone's *** free voxel in the octree map was set to have enough space for the drone to *** improved A*algorithm map was used to plan the route based on the *** simulation result showed that this efficient pathfinding method can reduce execution time and memory space *** the actual flight,a local path planning system was proposed to avoid obstacles in real time.

关键词： Indoor Visual SLAM Drone Octree Collision avoidance Path planning

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Multi-Layer Environmental Affordance Map for Robust Indoor Localization, Event Detection and Social Friendly Navigation

Multi-Layer Environmental Affordance Map for Robust Indoor L...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ping-Tsang Wu Chee-An Yu Shao-Hung Chan Ming-Li Chiang Li-Chen Fu Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Director of NTU Center for Artificial Intelligence & Advanced Robotics Taipei Taiwan

In this paper, we propose a novel system architecture called multi-layer environmental affordance map for social and service companion robots. Based on this architecture, robots can organize the perception and inference information efficiently and generate social friendly navigation strategies. In other words, robots are able to strengthen their perception and inference abilities to interact with domestic environment and users under our efficient framework. The main feature of this architecture is that the relations between layers can be viewed as affordances to improve the accuracy and the robustness of the detection and inference. The results show that our architecture achieves robust indoor localization, scene localization, human event detection and socially friendly navigation in real time under limited computational resource.

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CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

arXiv

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arXiv 2022年

作者： Nwoye, Chinedu Innocent Alapatt, Deepak Yu, Tong Vardazaryan, Armine Xia, Fangfang Zhao, Zixuan Xia, Tong Jia, Fucang Yang, Yuxuan Wang, Hao Yu, Derong Zheng, Guoyan Duan, Xiaotian Getty, Neil Sanchez-Matilla, Ricardo Robu, Maria Zhang, Li Chen, Huabin Wang, Jiacheng Wang, Liansheng Zhang, Bokai Gerats, Beerend Raviteja, Sista Sathish, Rachana Tao, Rong Kondo, Satoshi Pang, Winnie Ren, Hongliang Abbing, Julian Ronald Sarhan, Mohammad Hasan Bodenstedt, Sebastian Bhasker, Nithya Oliveira, Bruno Torres, Helena R. Ling, Li Gaida, Finn Czempiel, Tobias Vilaça, João L. Morais, Pedro Fonseca, Jaime Egging, Ruby Mae Wijma, Inge Nicole Qian, Chen Bian, Guibin Li, Zhen Balasubramanian, Velmurugan Sheet, Debdoot Luengo, Imanol Zhu, Yuanbo Ding, Shuai Aschenbrenner, Jakob-Anton van der Kar, Nicolas Elini Xu, Mengya Islam, Mobarakol Seenivasan, Lalithkumar Jenke, Alexander Stoyanov, Danail Mutter, Didier Mascagni, Pietro Seeliger, Barbara Gonzalez, Cristians Padoy, Nicolas ICube University of Strasbourg CNRS France IHU Strasbourg France Department of Computer Science University of Chicago United States Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Management Hefei University of Technology Hefei China Institue of Medical Robotics Shanghai Jiao Tong University Shanghai China Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Digital Surgery A Medtronic Company London United Kingdom Institute of Automation Chinese Academy of Sciences China Department of Computer Science School of Informatics Xiamen University Xiamen China Johnson & Johnson Meander Medical Centre Netherlands Indian Institute of Technology Kharagpur India Muroran Institute of Technology Japan Department of Biomedical Engineering National University of Singapore Singapore Department for Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Germany 2Ai School of Technology IPCA Barcelos Portugal School of Medicine University of Minho Braga Portugal Algoritimi Center School of Engineering University of Minho Guimeraes Portugal Technical University of Munich Germany Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Wellcome EPSRC Center for Interventional and Surgical Science University College London United Kingdom Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy University Hospital of Strasbourg France

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity;yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. © 2022, CC BY-NC-SA.

关键词： Deep learning

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A robust and accurate SLAM algorithm for omni-directional mobile robots based on a novel 2.5D lidar device 13

A robust and accurate SLAM algorithm for omni-directional mo...

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13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Yang, Yu Yang, Guilin Tian, Yingzhong Zheng, Tianjiang Li, Long Wang, Zhenhua Ningbo Institute of Materials Technology and Engineering CAS Ningbo315201 China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Jiangsu Provincial Key Laboratory of Advanced Robotics Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou215123 China

ISBN: (纸本)9781538637579

Lidars are commonly employed in mobile robot SLAM applications due to their high stability and high measuring accuracy. For a conventional 2D Lidar, only the planar points can be detected in once scan, which limits the robustness of SLAM algorithms. Omni-directional mobile robots with powered caster wheels have 3-DOF planar motion capabilities so that they are widely deployed in narrowly constrained environment. This paper presents a novel design of a 2.5D Lidar device, in which a 2D Lidar is mounted onto a 1-DOF liner stage that moves along the vertical direction. As a result, the 2.5D Lidar device can also perform vertical scanning within the motion range of the liner stage. Consequently, a robust SLAM algorithm based on the 2.5D Lidar device is proposed for the omni-directional mobile robot, where a frame-to-frame scan matching using ICP algorithm is employed to improve the robustness and accuracy of the scan matching. As the 2.5D points cloud is actually located in a belt-shaped space, the map can be finally constructed in a 2D mode to reduce the computational complexity. In addition, when an incorrect matching occurs, the odometry information of the mobile robot will be employed to further improve the robustness and accuracy of the scan matching algorithm. The effectiveness of the proposed approach is validated through computer simulations. © 2018 IEEE.

关键词： Optical radar

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Corrigendum to “Analyzing quasi-static fracture of notched magnesium ZK60 using notch fracture toughness and support vector machine” [Theoret. Appl. Fract. Mech. 121 (2022) 103463]

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Theoretical and Applied Fracture Mechanics 2022年 121卷

作者： Abdullah Alshaiji Jafar Albinmousa Mirco Peron Bandar AlMangour Usman Ali Mechanical Engineering Department King Fahd University of Petroleum & Minerals 31261 Dhahran Saudi Arabia Interdisciplinary Research Center for Advanced Materials King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Department of Mechanical and Industrial Engineering Norwegian University of Science and Technology Trondheim Norway Interdisciplinary Research Center for Intelligent Manufacturing & Robotics King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Researcher at K.A. CARE Energy Research & Innovation Center at Dhahran Saudi Arabia

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Long-Term Stable Hydrogen Production from Water and Lactic Acid via Visible-Light-Driven Photocatalysis in a Porous Microreactor

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Angewandte Chemie 2023年第32期135卷

作者： Xin-Yu Meng Jin-Jin Li Peng Liu Mingyu Duan Jing Wang Prof. Yin-Ning Zhou Prof. Yongbing Xie Prof. Zheng-Hong Luo Prof. Yun-Xiang Pan Department of Chemical Engineering School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai 200240 P. R. China School of Chemical Engineering East China University of Science and Technology Shanghai 200237 P. R. China Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing 100871 P. R. China Chemistry & Chemical Engineering Data Center Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 P. R. China

Photocatalytic hydrogen (H 2 ) production is significant to overcome challenges like fossil fuel depletion and carbon dioxide emission, but its efficiency is still far below that which is needed for commercialization. Herein, we achieve long-term stable H 2 bubbling production from water (H 2 O) and lactic acid via visible-light-driven photocatalysis in a porous microreactor (PP12); the catalytic system benefits from photocatalyst dispersion, charge separation, mass transfer, and dissociation of O−H bonds associated with H 2 O. With the widely used platinum/cadmium-sulfide (Pt/CdS) photocatalyst, PP12 leads to a H 2 bubbling production rate of 602.5 mmol h −1 m −2 , which is 1000 times higher than that in a traditional reactor. Even when amplifying PP12 into a flat-plate reactor with an area as large as 1 m 2 and extending the reaction time to 100 h, the H 2 bubbling production rate still remains at around 600.0 mmol h −1 m −2 , offering great potential for commercialization.

关键词： H2 H2O Photocatalysis Polymer Reactor

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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A Vision Defect Detection Algorithm based on Topological Mapping for High-Resolution Flexible Integrated Circuit Substrates

A Vision Defect Detection Algorithm based on Topological Map...

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第三十八届中国控制会议

作者： Zhiyan Zhong Yueming Hu South China University of Technology College of Automation Science and Engineering Engineering Research Center for Precision Electronic Manufacturing Equipments of Ministry of Education Guangzhou Provincial Engineering Laboratory for Advanced Chip Intelligent Packaging Equipment

The conventional methods of visual defect detection heavily rely on the standard templates and features of an image. But the standard templates are difficult to obtain or even don't exist in many application fields, especially for the flexible Integrated Circuit(IC) substrates based on micro vision. To find a solution for the above problems, the authors have proposed a novel defect detection approach based on topology mapping. Firstly, topological mapping on the pixel values of the image is performed to remove the noise. Then, a morphological operation is used to clear small spots. Furthermore, binarization is introduced to display the defect of corrosion and remove the black background and surface texture structure of the substrate image. The experiments are implemented to prove that the proposed method has higher Peak Signal to Noise Ratio and produces better quality of image, which confirms the efficiency of the proposed algorithm.

关键词： Flexible integrated circuit packaging substrates topological mapping vision detection oxidation region detection

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A Generative Model of Underwater Images for Active Landmark Detection and Docking

A Generative Model of Underwater Images for Active Landmark ...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shuang Liu Mete Ozay Hongli Xu Yang Lin Takayuki Okatani State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Graduate School of Information Sciences Tohoku University Sendai Miyagi Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

Underwater active landmarks (UALs) are widely used for short-range underwater navigation in underwater robotics tasks. Detection of UALs is challenging due to large variance of underwater illumination, water quality and change of camera viewpoint. Moreover, improvement of detection accuracy relies upon statistical diversity of images used to train detection models. We propose a generative adversarial network, called Tank-to-field GAN (T2FGAN), to learn generative models of underwater images, and use the learned models for data augmentation to improve detection accuracy. To this end, first a T2FGAN is trained using images of UALs captured in a tank. Then, the learned model of the T2FGAN is used to generate images of UALs according to different water quality, illumination, pose and landmark configurations (WIPCs). In experimental analyses, we first explore statistical properties of images of UALs generated by T2FGAN under various WIPCs for active landmark detection. Then, we use the generated images for training detection algorithms. Experimental results show that training detection algorithms using the generated images can improve detection accuracy. In field experiments, underwater docking tasks are successfully performed in a lake by employing detection models trained on datasets generated by T2FGAN.

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