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IEEE International Conference on robotics and Biomimetics

作者： Songyu Yuan Jing Zhang Chen Pan Chunlong Zhang Minhong Wan Tao Zheng Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

The field of Simultaneous Localization and Mapping(SLAM) has witnessed significant advancements in recent years. However, due to the difficulty of data obtains and ground-truth makes, there is a lack of real indoor scenarios datasets, especially the scenarios with less-texture, repeat-texture, extreme light condition, causing the slowly progress in indoor SLAM. In this paper, we present a dataset captured in a real indoor exhibition hall with diverse type of sensor data. Which contributes to diverse school of SLAM that using different sensors. We provide ground-truth 6DoF pose captured by motion capture devices, RGB-D data captured by a RealSense D455 camera. Laser-data captured by Ouster. And a high-resolution color point cloud captured by a laser scanner. This dataset offers three distinct sources of ground truth from real-world scenarios. It combines motion capture, ICP based laser localization, and visual localization methods to provide comprehensive and accurate reference data. This dataset is designed for evaluating positioning methods that employ various types of sensors, moving beyond a singular matrix-based evaluation approach. Besides, We show results of several state-of-the-art SLAM method in our datasets, illustrates the utility of our dataset and served as a reference for other researcher. We converge LiDAR-based SLAM with ground truth, made a good closure. An ensemble approach-based Visual SLAM, achieves results of odometry errors of 4% and loop closure position errors of 40 cm. Additionally, visual localization demonstrated an absolute position error of 21 cm using high-resolution point clouds. We highlight the potentials of our indoor datasets in LiDAR SLAM, RGB-D SLAM, and visual localization and combines method. The datasets are available at https://***/IMBAfarmer09/ExhibitionDataset

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A Joint Tracking System: Robot is Online to Access Surveillance Views

A Joint Tracking System: Robot is Online to Access Surveilla...

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IEEE International Conference on robotics and Biomimetics

作者： Zheyuan Lin Shanshan Ji Wen Wang Mengjie Qin Rong Yang Minhong Wan Jason Gu Te Li Chunlong Zhang Research Center for Intelligent Robotics Zhejiang Laboratory Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

The application of robots in social life, equipped with sensors and actuators and embedded with AI, assists people in all aspects. However, the first perspective of the robot horizon is heavily constrained, which weakens its performance. A joint tracking system is designed and built to deal with this, by integrating a surveillance system with the robot visual, providing a third perspective. This system takes one horizontal view and two top views from various directions as inputs and matches a person among the frames and in time sequence. In order to deal with the identity match with a huge visual feature gap, a special dataset is collected, simultaneously labeling identities from a mobile robot perspective and multiple indoor static surveillance monitors. The experiment shows that such match is a task worth exploring that can be better handled by training on our dataset than existing open source Re-identification (Re-id) datasets. Moreover, in the real scenario, this system improves the performance on issues like in and out of the robot’s field of vision and heavy occlusion by people or objects.

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Mathematical Modelling of a Four-Fingered Metamorphic Anthropomorphic Hand on Precision Grasp

Mathematical Modelling of a Four-Fingered Metamorphic Anthro...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Tun Wang Yen-Hua Lin Jian S. Dai Department of Mechanical and Electrical Engineering Centre of Advanced Oil and Gas Equipment China University of Petroleum (Beijing) Beijing China Department of Engineering Centre for Robotics Research King's College London London UK Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

For a metamorphic robotic hand with a foldable palm, its finger operation planes change with palm configurations. Although this reconfigurable design can enhance the grasp flexibility, manipulation workspace and application adaptability, the hand's grasp stability cannot be guaranteed when the palm reconfigures. Especially for the four-fingered metamorphic hand performing precision grasps in anthropomorphic postures, improper control of the palm configurations can result in unsuccessful grasps, which may even lead to hand damage. This paper first displays the workspace of the four-fingered anthropomorphic hand for generating precision grasp gestures. Based on it, force-closure/-balance analyses are performed, deriving the effective precision grasp workspace for typical anthropomorphic postures classified by built mathematical definition. Grasp levels are further discussed by introducing an evaluation method which considers the minimum friction coefficient (MFC) as the quality metric. The proposed scheme gives a way to depict the grasp performance of every robotic hand configuration, without considering specific objects that are to be grasped. Dynamic changes in postures' grasp performance with palm configurations are exhibited in the end.

关键词： Measurement Friction Mathematical models Stability analysis Robots Petroleum

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Adaptive Beamforming Based on Interference-Plus-Noise Covariance Matrix Reconstruction for Speech Separation

Adaptive Beamforming Based on Interference-Plus-Noise Covari...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yongxiong Xiao Shiqiang Zhu Te Li Minhong Wan Wei Song Jason Gu Qiang Fu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

Estimating the interference-plus-noise covariance matrix (INCM) is critical for the robustness of the minimum variance distortionless response (MVDR) beamformer. Existing INCM reconstruction methods are computationally intensive and not suitable for real-time speech separation. We propose a singular value decomposition (SVD) based INCM reconstruction method for speech separation. The spatial covariance matrix (SCM) for each source is obtained by rank-1 approximation using the nominal steering vector (SV) or the pre-measured relative transfer function (RTF). The INCM used to separate each source is reconstructed as the sum of the covariance matrices of interference and spherical isotropic noise. The proposed method is evaluated using the mixed signal received by a circular array with six microphones placed in a simulated reverberation chamber. The results show that the proposed method has comparable sound quality performance to the reference method, but requires much less computation.

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RFFCE: Residual Feature Fusion and Confidence Evaluation Network for 6DoF Pose Estimation

RFFCE: Residual Feature Fusion and Confidence Evaluation Net...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Qiwei Meng Shanshan Ji Shiqiang Zhu Tianlei Jin Te Li Jason Gu Wei Song Research Center for Intelligent Robotics Zhejiang Lab Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

In this paper, we propose a novel RGBD-based object 6DoF pose estimation network - RFFCE. It is a two-stage method that firstly leverages deep neural networks for feature extraction and object points matching, and then the geometric principles are utilized for final pose computation. Our approach consists of three primary innovations: residual feature fusion for representative RGBD feature extraction; confidence evaluation and confidence-based paired points offsets regression for self-evaluation and self-optimization respectively. Their effectiveness is verified through an ablation study, and our RFFCE achieves the SOTA performance on LineMOD, Occlusion-LineMOD and YCB-Video datasets. Additionally, we also conduct a real-world object grasping experiment for visualization and qualitative evaluation of the RFFCE.

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Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots

Fuzzy Object Ambiguity Determination and Human Attention Ass...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kevin Fan Melanie Jouaiti Kerstin Dautenhahn Chrystopher L. Nehaniv Department of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada Departments of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada

Domestic service robots have the promising potential of bringing significant services to the general population, and more importantly, successful applications of universal domestic service robots can potentially help mitigate critical societal issues such as senior care. In order to do so, domestic service robots need to integrate seamlessly into home environments. However, home environments are dynamic, complex and filled with personal items. Therefore, ambiguity can quickly arise for robots operating in such rich environments. In this paper, we propose an object ambiguity determination system that can determine the level of ambiguity in robot object selection tasks with fuzzy logic data integration. Additionally, we propose a functional human attention assessment system with fuzzy logic that enables the robot to determine user attention before committing to general disambiguation processes. Our preliminary results show that the proposed fuzzy logic inference systems can reliably estimate the robot object selection task ambiguity from object confidence level and the number of potential target objects that satisfy the user's command. Furthermore, fuzzy inference is applied to decide human eye gaze direction robustly. These subsystems can be utilized in the context of human-robot interaction to guide the robot when to seek feedback from a human partner in order to disambiguate reference in domestic service tasks. The source code of all proposed systems is available publicly on GitHub. 1

关键词： Fuzzy logic Service robots Source coding Sociology Human-robot interaction Data integration Reliability

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LiDAR Simulation for Performance Evaluation of UAS Detect and Avoid

LiDAR Simulation for Performance Evaluation of UAS Detect an...

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Riordan, James Manduhu, Manduhu Black, Julie Dow, Alexander Dooly, Gerard Matalonga, Santiago Almada Research Centre School of Computing Engineering and Physical Science University of the West of Scotland Lanarkshire United Kingdom Centre for Robotics and Intelligent Systems University of Limerick Department of Electronic and Computer Engineering

ISBN: (纸本)9780738131153

The solution to mitigating risks associated with beyond Visual Line of Sight (BVLOS) operations of Unmanned Aerial System (UAS) generally focuses on the use of advanced Unmanned Traffic Management (UTM) systems. However, this solution does not take into account other uncooperative objects in the airspace. A more robust approach is to have UTM integrations coupled with onboard machine vision which is tied to automated collision avoidance systems. Future BVLOS regulations in urban situations may require robust embedded software that is capable of detecting air collision hazards in realtime at near and far ranges as uncooperative small aircraft and other unpredictable small objects with fast-changing and unscheduled trajectories pose significant hazards to UAS. This work presents the concept and initial prototyping of a Digital Twin to evaluate the capability of UAS mounted LiDAR to detect small-object air collision risks. A Digital Twin of the Port of Hamburg is augmented with typical port and harbour aerial hazards such as birds, drones, helicopters, and low flying aircraft. The use case scenarios are created in Maya and Unity, with Optix ray tracing of typical LiDAR imaging configurations used to replicate the cause and effect relationship between different LiDAR specifications and their response to small flying objects. Our results demonstrate the inhomogeneous point clouds generated at different spatial-temporal parts of the LiDAR scanning cycle and field of view. These results confirm the challenges of detecting small uncooperative objects by LiDAR. © 2021 IEEE.

关键词： Hazards

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KGNet: Knowledge-Guided Networks for Category-Level 6D Object Pose and Size Estimation

KGNet: Knowledge-Guided Networks for Category-Level 6D Objec...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Qiwei Meng Jason Gu Shiqiang Zhu Jianfeng Liao Tianlei Jin Fangtai Guo Wen Wang Wei Song Zhejiang Lab Research Center for Intelligent Robotics Research Institute of Interdis-ciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

Despite the giant leap made in object 6D pose estimation and robotic grasping under structured scenarios, most approaches depend heavily on the exact CAD models of target objects beforehand, thereby limiting their wide applications. To address this, we propose a novel knowledge-guided network - KGNet to estimate the pose and size of category-level unseen objects. This network includes three primary innovations: knowledge-guided categorical model generation, pointwise deformation probability matrix and synergetic RGBD feature fusion, with the former two leveraging categorical object knowledge for unseen object reconstruction and the latter one facilitating pose-sensitive feature extraction. Exten-sive experiments on CAMERA25 and REAL275 verify their effectiveness, and KGNet achieves the SOTA performance on these two acknowledged benchmarks. Additionally, a real-world robotic grasping experiment is conducted, and its results further qualitatively prove the practicability and robustness of KGNet.

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Pre-trained Deep Learning Networks for Advanced Visible Imagery Drone Detection and Recognition

Pre-trained Deep Learning Networks for Advanced Visible Imag...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Hassan J. Al dawasari Muhammad Bilal Muhammad Moinuddin Kamran Arshad Khaled Assaleh Engineering Systems and Electrical and Computer Engineering Department Centre of Excellence in Intelligent King Abdulaziz University Jeddah Saudi Arabia Artificial Intelligence Research Centre Ajman University Ajman UAE

In this paper, we introduce a state-of-the-art deep learning technique designed to accurately differentiate between drones and birds. This technique is particularly effective in reducing hazards associated with unauthorized drones, especially in airport environments where such drones can cause significant flight disruptions. Our approach involves the utilization of a meticulously compiled image dataset for testing, yielding results that surpass previous detection methods outlined in existing literature. Among the models evaluated, ResNet18 emerges as a standout, achieving an impressive average precision (AP) of 0.739 in medium area ratios. A key feature of our method is its ability not only to detect drones but also to precisely distinguish them from birds. The dataset employed in this research is derived from the publicly accessible real-world data of the 2020 Drone vs. Bird Detection Challenge.

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