检索结果-内蒙古大学图书馆

IEEE International Conference on robotics and Automation (ICRA)

作者： Shuai Wang Jiahu Qin Menglin Li Yaonan Wang University of Science and Technology of China Hefei China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China

Learning driving policies using an end-to-end network has been proved a promising solution for autonomous driving. Due to the lack of a benchmark driver behavior dataset that contains both the visual and the LiDAR data, existing works solely focus on learning driving from visual sensors. Besides, most works are limited to predict steering angle yet neglect the more challenging vehicle speed control problem. In this paper, we propose a novel end-to-end network, FlowDriveNet, which takes advantages of sequential visual data and LiDAR data jointly to predict steering angle and vehicle speed. The main challenges of this problem are how to efficiently extract driving-related information from images and point clouds, and how to fuse them effectively. To tackle these challenges, we propose a concept of point flow and declare that image optical flow and LiDAR point flow are significant motion cues for driving policy learning. Specifically, we first create an enhanced dataset that consists of images, point clouds and corresponding human driver behaviors. Then, in FlowDriveNet, a deep but efficient visual feature extraction module and a point feature extraction module are utilized to extract spatial features from optical flow and point flow, respectively. Additionally, a novel temporal fusion and prediction module is designed to fuse temporal information from the extracted spatial feature sequences and predict vehicle driving commands. Finally, a series of ablation experiments verify the importance of optical flow and point flow and comparison experiments show that our flow-based method outperforms the existing image-based approaches on the task of driving policy learning.

关键词： Image motion analysis Computer vision visualization Laser radar Fuses Predictive models Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Research on graph-based SLAM for UVC disinfection robot

Research on graph-based SLAM for UVC disinfection robot

引用

2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Tan, Xuan Zhang, Hui Zhou, Xidong Zhong, Hang Liu, Li College of Electrical and Information Engineering Changsha University of Science and Technology Hunan Province410012 China Hunan University School of Robotics Visual Perception and Control Technology National Engineering Laboratory Hunan Province410082 China College of Electrical and Information Engineering Hunan University Hunan Province410082 China

ISBN: (纸本)9781665436786

With the emergence of the COVID-19 pandemic, more and more non-contact mobile disinfection robots have appeared in the medical field, which have made great contributions to the fight against the epidemic. Aiming at the problems of single disinfection method, single application scenario, low degree of intelligence and lack of autonomous mobile disinfection in existing disinfection robots, this paper proposes and designs a disinfection and epidemic prevention intelligent robot called Aimi-robot UVC, which is based on graph-optimized slam algorithm to complete the localization and map creation functions of the robot in the unknown environment. After testing in the isolated single ward of the hospital, the realtime localization accuracy reaches 0.04m, which provides high-precision and high-reliability localization for the disinfection robot in the hospital scene and has great practical significance for the application of intelligent disinfection robots in epidemic prevention and control. © 2021 IEEE.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Admittance Based robot Force control Framework for Server Board Assembly

Admittance Based Robot Force Control Framework for Server Bo...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Yunlong Ma Yaonan Wang Yiming Jiang Xianen Zhou Ge Zhu Qing Zhu Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Xiangjiang Laboratory Hunan China College of Electrical and Information Engineering Hunan University Changsha China School of Robotics Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan University Changsha China ZTE Corporation Shenzhen China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

In server board assembly tasks, the effect of vision-based robot assembly schemes is not ideal due to the small installation gap and the blocking of vision. Adding force sensors and force controllers can be a good solution to the above problems and increase the flexibility in the assembly process. In this paper, we propose a force control strategy applied to a server board assembly task. The method is divided into two main parts. In the first part, the zero offset of the force sensor and the load gravity are calibrated and compensated, so that the external force on the load is accurately obtained. In the second part, the admittance controller is designed to achieve compliant behavior between the robot and the environment. Finally, the experimental verification of the board insertion is carried out on the experimental platform. Experimental results verify the effectiveness and practicability of the proposed method.

关键词： Process control robot sensing systems Manipulators Servers Force sensors Admittance Force control

来源：评论

学校读者我要写书评

暂无评论

Domain-invariant Prototypes for Semantic Segmentation

arXiv

引用

arXiv 2022年

作者： Yang, Zhengeng Yu, Hongshan Sun, Wei Li-Cheng Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu410082 China The Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada The Department of Computer Science The University of Western Australia PerthWA6009 Australia

Deep Learning has greatly advanced the performance of semantic segmentation, however, its success relies on the availability of large amounts of annotated data for training. Hence, many efforts have been devoted to domain adaptive semantic segmentation that focuses on transferring semantic knowledge from a labeled source domain to an unlabeled target domain. Existing self-training methods typically require multiple rounds of training, while another popular framework based on adversarial training is known to be sensitive to hyper-parameters. In this paper, we present an easy-to-train framework that learns domain-invariant prototypes for domain adaptive semantic segmentation. In particular, we show that domain adaptation shares a common character with few-shot learning in that both aim to recognize some types of unseen data with knowledge learned from large amounts of seen data. Thus, we propose a unified framework for domain adaptation and few-shot learning. The core idea is to use the class prototypes extracted from few-shot annotated target images to classify pixels of both source images and target images. Our method involves only one-stage training and does not need to be trained on large-scale un-annotated target images. Moreover, our method can be extended to variants of both domain adaptation and few-shot learning. Experiments on adapting GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes show that our method achieves competitive performance to state-of-the-art. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Quantum machine learning for multiclass classification beyond kernel methods

arXiv

引用

arXiv 2024年

作者： Ding, Chao Wang, Shi Wang, Yaonan Gao, Weibo College of Electrical and Information Engineering Hunan University Changsha410082 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore The Photonics Institute Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

Quantum machine learning is considered one of the current research fields with great potential. In recent years, Havlíček et al. [Nature 567, 209-212 (2019)] have proposed a quantum machine learning algorithm with quantum-enhanced feature spaces, which effectively addressed a binary classification problem on a superconducting processor and offered a potential pathway to achieving quantum advantage. However, a straightforward binary classification algorithm falls short in solving multiclass classification problems. In this paper, we propose a quantum algorithm that rigorously demonstrates that quantum kernel methods enhance the efficiency of multiclass classification in real-world applications, providing quantum advantage. To demonstrate quantum advantage, we design six distinct quantum kernels within the quantum algorithm to map input data into quantum state spaces and estimate the corresponding quantum kernel matrices. The results from quantum simulations reveal that the quantum algorithm outperforms its classical counterpart in handling six real-world multiclass classification problems. Furthermore, we leverage a variety of performance metrics to comprehensively evaluate the classification and generalization performance of the quantum algorithm. The results demonstrate that the quantum algorithm achieves superior classification and better generalization performance relative to classical counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum efficiency

来源：评论

学校读者我要写书评

暂无评论

Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving

arXiv

引用

arXiv 2024年

作者： Shi, Hao Wang, Song Zhang, Jiaming Yin, Xiaoting Wang, Zhongdao Wang, Guangming Zhu, Jianke Yang, Kailun Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Huawei Noah’s Ark Lab. Canada The Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining labels using explicit multi-view geometry and integrating sensor bias, especially to increase the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC models to a level even surpassing that of LiDAR-based onboard SSC models. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly available at https://***/MasterHow/OccFiner. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction

arXiv

引用

arXiv 2024年

作者： Li, Siyu Lin, Jiacheng Shi, Hao Zhang, Jiaming Wang, Song Yao, You Li, Zhiyong Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The College of Computer Science Zhejiang University Hangzhou310027 China The USC Viterbi School of Engineering The University of Southern California Los AngelesCA90089 United States

Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code is available at https://***/lynn-yu/DTCLMapper. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Geometric Formation Tracking of Aerial robot Swarms Without Linear Velocity Measurements Over Directed Networks

Geometric Formation Tracking of Aerial Robot Swarms Without ...

引用

IEEE International Conference on robotics and Biomimetics

作者： Jie Lin Yaonan Wang Zhiqiang Miao Rafael Fierro College of Electrical and Information Engineering Hunan University Changsha China The National Engineering Laboratory for Robot Visual Perception and Control Changsha China MARHES Lab University of New Mexico Albuquerque New Mexico USA

ISBN: (纸本)9781665405362

This paper focuses on the formation tracking problem of aerial robot swarms without linear velocity measurements over directed interaction topologies. A distributed formation controller is designed for a quadrotor swarm under a directed graph using the hierarchical control structure. First, the outer-loop controller governs the translational motion to achieve the desired formation, where the filter-like auxiliary dynamic system is designed for each quadrotor to overcome the lack of the linear velocity measurements. Next, based on the geometric attitude representing, an almost-global inner-loop controller is proposed to regulate the attitude of each quadrotor, which is not only coordinate-free but also the strongest possible controller in terms of the region of convergence. Furthermore, necessary and sufficient conditions for the formation tracking problem over the directed graph are presented so that the stability analysis is guaranteed. Finally, simulation results are provided to show the effectiveness of the proposed control algorithms.

关键词： Sufficient conditions Attitude control Tracking Simulation robot kinematics Directed graphs Autonomous aerial vehicles

来源：评论

学校读者我要写书评

暂无评论

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

引用

arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

arXiv

引用

arXiv 2022年

作者： Liu, Ruiping Yang, Kailun Roitberg, Alina Zhang, Jiaming Peng, Kunyu Liu, Huayao Wang, Yaonan Stiefelhagen, Rainer The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Robotics The National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The Institute for Visual Computing ETH Zurich Zurich8092 Switzerland NIO Shanghai201804 China

Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training durations. To lift this constraint, we look at efficient semantic segmentation from a perspective of comprehensive knowledge distillation and aim to bridge the gap between multi-source knowledge extractions and transformer-specific patch embeddings. We put forward the Transformer-based Knowledge Distillation (TransKD) framework which learns compact student transformers by distilling both feature maps and patch embeddings of large teacher transformers, bypassing the long pre-training process and reducing the FLOPs by >85.0%. Specifically, we propose two fundamental modules to realize feature map distillation and patch embedding distillation, respectively: (1) Cross Selective Fusion (CSF) enables knowledge transfer between cross-stage features via channel attention and feature map distillation within hierarchical transformers;(2) Patch Embedding Alignment (PEA) performs dimensional transformation within the patchifying process to facilitate the patch embedding distillation. Furthermore, we introduce two optimization modules to enhance the patch embedding distillation from different perspectives: (1) Global-Local Context Mixer (GL-Mixer) extracts both global and local information of a representative embedding;(2) Embedding Assistant (EA) acts as an embedding method to seamlessly bridge teacher and student models with the teacher’s number of channels. Experiments on Cityscapes, ACDC, NYUv2, and Pascal VOC2012 datasets show that TransKD outperforms state-of-the-art distillation frameworks and rivals the time-consuming pre-training method. The source code is publicly available at https://***/RuipingL/TransKD. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：