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A Degeneracy-Aware LiDAR-Inertial-Visual SLAM for Degenerate Environments

IEEE Sensors Journal 2025年第11期25卷 19376-19385页

作者： Zhu, Hongwei Zhang, Guobao Huang, Yongming Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

Different sensors may experience degeneracy in specific scenarios, which can lead to failures in multi-sensor fusion optimization. To address the challenges of localization and mapping under such degeneracy conditions, this paper proposes a LiDAR-Inertial-Visual SLAM system with integrated degeneracy awareness. In the frontend, the system utilizes the ESIKF for tracking and incorporates a sensor degeneracy detection method to ensure that degraded sensor data do not affect the state error update. In the backend, we replace the conventional point cloud map with an adaptive voxel map to accelerate the retrieval of planar point cloud, and merge LiDAR planar features within a sliding window via region growing algorithm. Furthermore, the point-to-plane distance formula is optimized via the Rayleigh quotient theorem, which reduces the dimensionality of the optimization variables and simplifies the optimization process. Finally, we evaluate the proposed system on several challenging datasets and compare it with various state-of-the-art sensor fusion systems. The experimental results demonstrate that the proposed system achieves high localization accuracy in the majority of challenging scenarios. © 2001-2012 IEEE.

关键词： Inertial navigation systems

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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End-to-end multi-task reinforcement learning-based UAV swarm communication attack detection and area coverage

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Knowledge-Based systems 2025年 316卷

作者： Yu, Jin Zhang, Ya Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

This paper proposes an innovative end-to-end multi-task reinforcement learning framework designed to enhance the security and operational stability of UAVs in complex communication attacks environments. In scenarios involving multiple UAV coverage, the framework classifies attack types and predicts UAV behaviors, which enables rapid response and compromised data recovery. First, a spatiotemporal attention mechanism is employed to strengthen the network by capturing temporal dependencies in the communication data and improving the recognition of hybird attack mode, thereby facilitating the integration of detection and repair. Second, the causal inference mechanism introduced in this study extracts true causal relationships from interference, which ensures the accuracy and stability of action predictions for area coverage under attack conditions. Finally, the entire process is modeled as a multi-task Markov decision process, optimizing overall decision-making efficiency. Additionally, the dynamic pheromone induction mechanism is proposed to realize the adaptive regional cooperative coverage of multiple UAVs. The experimental results demonstrate that the proposed algorithm can effectively detect hybrid attacks and predict UAV actions, protecting the multi-UAV cooperative area coverage. © 2025

关键词： Reinforcement learning

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network 26

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 IEEE.

关键词： Frequency domain analysis

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Context-Aided Semantic-Aware Self-Alignment for Video-Based Person Re-Identification

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Ran, Zhidan Xiao, Zhiyao Lu, Xiaobo Wei, Xuan Liu, Wei Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China

Video-based person re-identification (Re-ID) aims at associating the video sequences of the identical person across multiple cameras. The ubiquitous appearance misalignment poses a major obstacle for video person Re-ID. Existing alignment-based methods generally rely on off-the-shelf semantic parsing models to locate visible human parts, which ignore identifiable personal belongings and cannot handle various interferences (e.g., pedestrian detection errors and occlusions) in video clips. In this work, we propose a novel framework termed Context-Aided Semantic-Aware Self-Alignment (CSSA) for video-based person Re-ID. First, we propose to jointly learn pixel-level part-aligned representations and semantic-aligned global-level representations in an end-to-end manner. Unlike most existing approaches that depend on prior information in terms of pose for part estimation, CSSA can locate different body parts and achieve the pixel-level semantic alignment without extra human topology semantics. Second, a Context-Aided Region Enhancement (CARE) module is proposed to efficiently highlight macro-visual patterns associated with the target pedestrian and suppress noise caused by factors like background clutters and occlusions. Third, we propose a Semantic-Aware Global Feature Alignment (SGFA) method for generating pair-wise semantic-aligned global representations, which play an essential role in both the training and inference phases. Extensive experimental results on multiple challenging benchmarks indicate the superiority and effectiveness of the proposed CSSA. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Rethinking iterative stereo matching from a diffusion bridge model perspective

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Pattern Recognition 2025年 167卷

作者： Shi, Yuguang Zhou, Sifan Wang, Wei Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

Stereo matching is vital for 3D perception in vision and robotics. Although iteration-based stereo matching has demonstrated competitive performance, it also has inherent limitations, such as the discrete nature of the iteration process, which can lose geometric structure information, and the dependence on a large number of iterations. In this paper, we present a novel iterative-based framework, named Diffusion Models for Iterative Optimization (DMIO). DMIO introduces a Time-based Update Network (T-UN) that embeds time encoding into sub-modules within the update network. This approach correlates temporal and disparity outputs and effectively integrates diffusion models into the iterative framework. This design enables DMIO to allow features containing fine details to be transmitted during the update process and can be seamlessly embedded into most iterative methods. We further design a Motion Agent Attention (MAA) module in the update network, which generates a set of attention weights for the local cost volume to filter redundant information. In addition, we introduce channel attention into the context network, which can generate more expressive features to improve accuracy. Experiments on several public benchmarks for stereo matching show that DMIO achieves over an 8.5% improvement over competing method and requires only 8 iterations to achieve state-of-the-art performance. © 2025 Elsevier Ltd

关键词： Stereo image processing

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Mechanical and Geometric Properties of Nanosprings Based on Carbon Nanotubes with Dislocation Dipoles

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ACS Applied Nano Materials 2025年第18期8卷 9192-9200页

作者： Lei, Xiao-Wen Koyane, Jumpei Lu, Tong Matsuzaki, Kenichiro Yan, Yi Shi, Jin-Xing Fujii, Toshiyuki School of Materials and Chemical Technology Institute of Science Tokyo 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Department of Production Systems Engineering and Sciences Komatsu University Nu 1-3 Shicyomachi Ishikawa Komatsu923-8511 Japan

Carbon nanotubes (CNTs) are lightweight materials with excellent mechanical, electrical, and thermal conductivity, which make helical CNTs promising candidates for applications in mechanical components such as nanosprings. In this study, based on the continuous dislocation theory, we introduce dislocation dipoles into CNT with discrete crystal structures to design nanosprings, propose a method that integrates mechanics and geometry, and analyze the mechanical and geometric properties of nanosprings. In detail, this study investigated the feasibility of designing helical CNTs through spontaneous deformation induced by dislocation dipoles separated from Stone-Wales defects. Analytical models were constructed by systematically introducing dislocation dipoles into (4,4) CNTs of varying lengths. We combined mechanical and geometric methods, performed molecular dynamics simulations to obtain the stable structure of CNTs with dislocation dipoles, and employed differential geometry to investigate the Willmore energy of helically shaped CNTs. The results showed that when dislocation dipoles were introduced into straight CNTs, energy relaxation calculations demonstrated spontaneous deformation, leading to the formation of a spring-shaped CNT. The CNT length and dislocation type significantly affected the spring constant. Longer CNTs exhibited lower spring constants, and an increase in dislocation density led to a higher average potential energy. This study provides unique insights into the control of the CNT shape and mechanical and geometrical properties, contributing to the design of functional materials such as nanosprings. © 2025 The Authors. Published by American Chemical Society.

关键词： Geometry

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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Lyapunov-based stochastic model predictive control of stochastic nonlinear systems with input-delay using multi-dimensional Taylor network

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Journal of the Franklin Institute 2025年第9期362卷

作者： Zheng, Xiao-Yi Yan, Hong-Sen Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Jiangsu Nanjing210096 China School of Automation Southeast University Jiangsu Nanjing210096 China

Most of the existing stability control approaches to stochastic nonlinear systems with input-delay are based on robust mechanisms, which tend to be too conservative. Here, a Lyapunov-based stochastic model predictive control (LSMPC) scheme using the Fokker–Planck equation (FPE) is proposed to solve the problem. In the framework of the LSMPC approach, input-delay is compensated by the predictor feedback strategy, and the LSMPC method is extended to stochastic nonlinear systems with input-delay. FPE is introduced to describe the dynamic evolution of the probability density function (PDF) for stochastic nonlinear systems. The multi-dimensional Taylor network (MTN) is applied for approximating the solution of FPE. The proposed FPE-based LSMPC method proves to be capable of securing the probabilistic feasibility and stability of the closed-loop system. The closed-loop states turn out to succeed in converging to the small region around the origin in probability. The validity of the presented scheme is verified by an example. © 2025 The Franklin Institute

关键词： Predictive control systems

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