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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Sifei Wang Zhiqiang Miao Qiong Lin Jie Lin Jianxin Zeng Lin Chen Yaonan Wang College of Electrical and Information Engineering and the National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China

Trajectory tracking is a basic problem in quadrotor control. To achieve a better control effect, MPC is introduced in the control of quadrotors. In this paper, we introduce the error-state dynamic model of a quadrotor firstly. Then, we develop an MPC method via the Lie group of special euclidean group based on the error-state dynamic model of a quadrotor. The proposed method is established on a manifold, thus the smoothness of the quadrotor trackjectory can be guaranteed. Our scheme is developed in error-state, which means that we take the velocity of the target trajectory into consideration for the dynamic model building. Therefore the cost function of our proposed approach is convex while the traditional MPC method is not when tracking a nonlinear trajectory. At the end of this paper, we compare our proposed MPC method with the traditional MPC method in one hundred sets of simulations. In our proposed method, the errors of position and orientation converge faster than the errors in the traditional method. The simulation results show that our proposed method is superior to traditional MPC scheme while tracking a nonlinear trajectory with a random initial position and verity the correctness of our MPC method.

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Robust Stereo VIO Based on an Equivariant Filter for Complex Lighting Environments

Robust Stereo VIO Based on an Equivariant Filter for Complex...

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IEEE International Conference on robotics and Biomimetics

作者： Zhenhang Chen Zhiqiang Miao Chuancheng Wang Yaonan Wang College of Electrical and Information Engineering and the National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China

ISBN: (纸本)9781665481106

visual Inertial Odometry(VIO) is widely used in various fields. When lighting conditions change dramatically, the visual front-end is affected, resulting in performance degradation and even failure in some extreme scenarios. In this paper, a stereo VIO based on an equivariant filter(EqF) for complex lighting environments is proposed. We propose a new adaptive gamma correction method, which can effectively improve the quality of image sequence and ensure the real-time performance of the algorithm because of the image selection processing strategy. Our VIO uses a robust stereo visual front-end and a state-of-the-art equivariant filter at the back-end. Experiments showed that the system has higher accuracy and robustness than another EqF-based VIO algorithm(EqF_VIO), and can work in complex and extreme lighting environments.

关键词： Degradation visualization robot vision systems Lighting Filtering algorithms Robustness Real-time systems

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Memory-augment convolutional Autoencoder for unsupervised pavement crack classification

Memory-augment convolutional Autoencoder for unsupervised pa...

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2021 China Automation Congress, CAC 2021

作者： Wu, Tianyue Zhang, Hui Liu, Jiaxuan Chen, Yurong Yi, Junfei Zhang, Zouquan School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China School of Robotics also with Visual Perception and Control Technology National Engineering Laboratory Hunan University Changsha China School of Electrical and Information Engineering Hunan University Changsha China

ISBN: (纸本)9781665426473

In urban construction, transportation system is an important part. However, the pavement cracks will occur because of using in long time and some external force collision, which has impacts on the safety and reliability of the whole transportation system. Therefore, it is necessary to detect the pavement cracks. The formation of pavement cracks is slow, and it is not concentrated on one place, so it is difficult to obtain enough images containing cracks to train. We propose a new pre-training method based on self-supervision study, which can obtain features closer to the target task from normal pavement images. In addition, we combine a memory-augment convolutional autoencoder and a hard-threshold module to improve the accuracy of the crack classification. Experiments conducted on the Mendeley Concrete crack dataset and DeepCrack dataset demonstrate the good performance in pavement crack classification task. © 2021 IEEE

关键词： Learning systems

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Current Status,Challenges,and Prospects for New Types of Aerial robots

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engineering 2024年第10期41卷 19-34页

作者： Xidong Zhou Hang Zhong Hui Zhang Wei He Hean Hua Yaonan Wang College of Robotics Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China School of Intelligence Science and Technology Beijing University of Science and TechnologyBeijing 100083China

New types of aerial robots(NTARs)have found extensive applications in the military,civilian contexts,scientific research,disaster management,and various other *** with traditional aerial robots,NTARs exhibit a broader range of morphological diversity,locomotion capabilities,and enhanced operational ***,this study defines aerial robots with the four characteristics of morphability,biomimicry,multi-modal locomotion,and manipulator attachment as ***,this paper discusses the latest research progress in the materials and manufacturing technology,actuation technology,and perception and control technology of ***,the research status of NTAR systems is summarized,focusing on the frontier development and application cases of flapping-wing microair vehicles,perching aerial robots,amphibious robots,and operational aerial ***,the main challenges presented by NTARs in terms of energy,materials,and perception are analyzed,and the future development trends of NTARs are summarized in terms of size and endurance,mechatronics,and complex scenarios,providing a reference direction for the follow-up exploration of NTARs.

关键词： Aerial robot Morphability Biomimicry Perch Amphibious

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Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

Multi-Expert Adversarial Attack Detection in Person Re-ident...

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International Conference on Computer Vision (ICCV)

作者： Xueping Wang Shasha Li Min Liu Yaonan Wang Amit K. Roy-Chowdhury College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology China University of California

ISBN: (纸本)9781665428132

The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually in-conspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%).

关键词： Deep learning Computer vision Perturbation methods Neural networks Detectors Feature extraction Context modeling

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Sparse Optical Flow-Based Line Feature Tracking

arXiv

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arXiv 2022年

作者： Fu, Qiang Yu, Hongshan Ali, Islam Zhang, Hong National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University China Department of Computing Science University of Alberta Canada

In this paper we propose a novel sparse optical flow (SOF)-based line feature tracking method for the camera pose estimation problem. This method is inspired by the point-based SOF algorithm and developed based on an observation that two adjacent images in time-varying image sequences satisfy brightness invariant. Based on this observation, we redefine the goal of line feature tracking: track two endpoints of a line feature instead of the entire line based on gray value matching instead of descriptor matching. To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints;next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function;finally, connect the two tracked endpoints to generate a new line feature. The correspondence is established between the given and the new line feature. Compared with current descriptor-based methods, our TET method needs not to compute descriptors and detect line features repeatedly. Naturally, it has an obvious advantage over computation. Experiments in several public benchmark datasets show our method yields highly competitive accuracy with an obvious advantage over speed. The source code of our method is released at: https://***/cnqiangfu/TET. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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Accurate RGB-D SLAM in dynamic environments based on dynamic visual feature removal

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Science China(Information Sciences) 2022年第10期65卷 256-269页

作者： Chenxin LIU Jiahu QIN Shuai WANG Lei YU Yaonan WANG Department of Automation University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University

visual localization is considered an essential capability in robotics and has attracted increasing interest for the past few years. However, most proposed visual localization systems assume that the surrounding environment is static, which is difficult to maintain in real-world scenarios due to the presence of moving objects. In this paper, we present DFR-SLAM, a real-time and accurate RGB-D SLAM based on ORB-SLAM2 that achieves satisfactory performance in a variety of challenging dynamic scenarios. At the core of our system lies a motion consensus filtering algorithm estimating the initial camera pose and a graph-cut optimization framework combining long-term observations, prior information, and spatial coherence to jointly distinguish dynamic and static visual features. Other systems for dynamic environments detect dynamic components by using the information from short time-span frames, whereas our system uses observations from a long period of keyframes. We evaluate our system using dynamic sequences from the public TUM dataset, and the evaluation demonstrates that the proposed system outperforms the original ORB-SLAM2 system significantly. In addition, our system provides competitive localization accuracy with satisfactory real-time performance compared to closely related SLAM systems designed to adapt to dynamic environments.

关键词： SLAM dynamic environments indoor localization graph-cut robot navigation

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An intelligent route recommendation method for smart Internet-connected electric vehicles under the coupling of traffic and electricity

An intelligent route recommendation method for smart Interne...

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第42届中国控制会议

作者： JinhuanXie Dongqi Liu ZhenwuYang School of Electrical and Information Engineering Changsha University of Science and Technology National Engineering Research Center for Robot Visual Perception and Control Technology

Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario,this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model based on the road traffic congestion predicted by the big data of the Internet of *** model integrally balances the interests of users and power grid in the coupled road-electric network,and solves the optimal path scheme under different travel scenarios based on collaborative filtering algorithm and user feedback,considering the influence of vehicle departure time and remaining power in short time *** is experimentally demonstrated that the proposed route hybrid recommendation model has better planning effect compared with the single travel model.

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Towards Consistent Object Detection via LiDAR-Camera Synergy

Towards Consistent Object Detection via LiDAR-Camera Synergy

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Luo, Kai Wu, Hao Yi, Kefu Yang, Kailun Hao, Wei Hu, Rongdong College of Automotive and Mechanical Engineering Changsha University of Science and Technology China School of Traffic and Transportation Engineering Changsha University of Science and Technology China School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China Changsha Intelligent Driving Institute China

ISBN: (纸本)9781665410205

As human-machine interaction continues to evolve, the capacity for environmental perception is becoming increasingly crucial. Integrating the two most common types of sensory data, images, and point clouds, can enhance detection accuracy. Currently, there is no existing model capable of detecting an object's position in both point clouds and images while also determining their corresponding relationship. This information is invaluable for human-machine interactions, offering new possibilities for their enhancement. In light of this, this paper introduces an end-to-end Consistency Object Detection (COD) algorithm framework that requires only a single forward inference to simultaneously obtain an object's position in both point clouds and images and establish their correlation. Furthermore, to assess the accuracy of the object correlation between point clouds and images, this paper proposes a new evaluation metric, Consistency Precision (CP). To verify the effectiveness of the proposed framework, an extensive set of experiments has been conducted on the KITTI and DAIR-V2X datasets. The study also explored how the proposed consistency detection method performs on images when the calibration parameters between images and point clouds are disturbed, compared to existing post-processing methods. The experimental results demonstrate that the proposed method exhibits ex-cellent detection performance and robustness, achieving end-to-end consistency detection. The source code will be made publicly available at https://***/xifen523/COD. © 2024 IEEE.

关键词： Object detection

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Design and Analysis of a Synergy-Inspired Three-Fingered Hand

Design and Analysis of a Synergy-Inspired Three-Fingered Han...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Wenrui Chen Zhilan Xiao Jingwen Lu Zilong Zhao Yaonan Wang National Engineering Laboratory for Robot Vision Perception and Control Technology Hunan University Changsha China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Hand synergy from neuroscience provides an effective tool for anthropomorphic hands to realize versatile grasping with simple planning and control. This paper aims to extend the synergy-inspired design from anthropomorphic hands to multi-fingered robot hands. The synergy-inspired hands are not necessarily humanoid in morphology but perform primary characteristics and functions similar to the human hand. At first, the biomechanics of hand synergy is investigated. Three biomechanical characteristics of the human hand synergy are explored as a basis for the mechanical simplification of the robot hands. Secondly, according to the synergy characteristics, a three-fingered hand is designed, and its kinematic model is developed for the analysis of some typical grasping and manipulation functions. Finally, a prototype is developed and preliminary grasping experiments validate the effectiveness of the design and analysis.

关键词： Thumb robots Joints Muscles Grasping Electronics packaging

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