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Field robotics 2025年 2卷 971-1000页

作者： Pierre-Yves Lajoie Benjamin Ramtoula Fang Wu Giovanni Beltrame Department of Computer and Software Engineering Polytechnique Montréal Canada Oxford Robotics Institute Department of Engineering Science University of Oxford United Kingdom Desay SV Automotive Singapore

Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.

关键词： Robots Simultaneous localization and mapping Robot kinematics Robot sensing systems Collaboration Surveys Multi-robot systems Sensors Market research Trajectory

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WQuatNet: Wide range quaternion-based head pose estimation

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Journal of King Saud University - Computer and Information Sciences 2025年第3期37卷 1-14页

作者： Algabri, Redhwan Shin, Hyunsoo Abdu, Ahmed Bae, Ji-Hun Lee, Sungon Dept. of Computer Science & Engineering College of AI Convergence Sejong University Seoul 05006 South Korea Research Institute of Engineering and Technology Hanyang University Ansan 15588 South Korea School of Software Northwestern Polytechnical University Xi’an 710072 China Department of Robotics Hanyang University Ansan 15588 South Korea

Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360∘ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks;instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model. © The Author(s) 2025.

关键词： Deep neural network Full range of rotation Head pose estimation Quaternion

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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

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Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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SLFCNet: an ultra-lightweight and efficient strawberry feature classification network

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PeerJ Computer Science 2025年 11卷 e2085-e2085页

作者： Xu, Wenchao Wang, Yangxu Yang, Jiahao School of Electrical and Computer Engineering Nanfang College Guangzhou Guangdong Conghua China Department of Network technology Guangzhou Institute of Software Engineering Guangdong Conghua China College of Robotics Guangdong Polytechnic of Science and Technology Guangdong Zhuhai China

Background. As modern agricultural technology advances, the automated detection, classification, and harvesting of strawberries have become an inevitable trend. Among these tasks, the classification of strawberries stands as a pivotal juncture. Nevertheless, existing object detection methods struggle with substantial computational demands, high resource utilization, and reduced detection efficiency. These challenges make deployment on edge devices difficult and lead to suboptimal user experiences. Methods. In this study, we have developed a lightweight model capable of real-time detection and classification of strawberry fruit, named the Strawberry Lightweight Feature Classify Network (SLFCNet). This innovative system incorporates a lightweight encoder and a self-designed feature extraction module called the Combined Convolutional Concatenation and Sequential Convolutional (C3SC). While maintaining model compactness, this architecture significantly enhances its feature decoding capabilities. To evaluate the model’s generalization potential, we utilized a high-resolution strawberry dataset collected directly from the fields. By employing image augmentation techniques, we conducted experimental comparisons between manually counted data and the model’s inference-based detection and classification results. Results. The SLFCNet model achieves an average precision of 98.9% in the mAP@0.5 metric, with a precision rate of 94.7% and a recall rate of 93.2%. Notably, SLFCNet features a streamlined design, resulting in a compact model size of only 3.57 MB. On an economical GTX 1080 Ti GPU, the processing time per image is a mere 4.1 ms. This indicates that the model can smoothly run on edge devices, ensuring real-time performance. Thus, it emerges as a novel solution for the automation and management of strawberry harvesting, providing real-time performance and presenting a new solution for the automatic management of strawberry picking. Copyright 2025 Xu et al. Distributed und

关键词： Fruits

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Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks

arXiv

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arXiv 2025年

作者： Choi, Hyun-Ho Kim, Kangsoo Lee, Ki-Ho Lee, Kisong School of ICT Robotics & Mechanical Engineering Hankyong National University Anseong17579 Korea Republic of Department of Electrical and Software Engineering Schulich School of Engineering University of Calgary ABT2N 1N4 Canada School of the Electrical Engineering Chung-Ang University Seoul06974 Korea Republic of Department of Information and Communication Engineering Dongguk University Seoul04620 Korea Republic of

Accurate and real-time three-dimensional (3D) pose estimation is challenging in resource-constrained and dynamic environments owing to its high computational complexity. To address this issue, this study proposes a novel cooperative inference method for real-time 3D human pose estimation in mobile edge computing (MEC) networks. In the proposed method, multiple end devices equipped with lightweight inference models employ dual confidence thresholds to filter ambiguous images. Only the filtered images are offloaded to an edge server with a more powerful inference model for re-evaluation, thereby improving the estimation accuracy under computational and communication constraints. We numerically analyze the performance of the proposed inference method in terms of the inference accuracy and end-to-end delay and formulate a joint optimization problem to derive the optimal confidence thresholds and transmission time for each device, with the objective of minimizing the mean per-joint position error (MPJPE) while satisfying the required end-to-end delay constraint. To solve this problem, we demonstrate that minimizing the MPJPE is equivalent to maximizing the sum of the inference accuracies for all devices, decompose the problem into manageable subproblems, and present a low-complexity optimization algorithm to obtain a near-optimal solution. The experimental results show that a trade-off exists between the MPJPE and end-to-end delay depending on the confidence thresholds. Furthermore, the results confirm that the proposed cooperative inference method achieves a significant reduction in the MPJPE through the optimal selection of confidence thresholds and transmission times, while consistently satisfying the end-to-end delay requirement in various MEC environments. © 2025, CC BY-SA.

关键词： NP-hard

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High-resolution synthetic aperture imaging method and benchmark based on event-frame fusion

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Information Fusion 2025年 122卷

作者： Li, Siqi Li, Yipeng Liu, Yu-Shen Du, Shaoyi Yong, Jun-Hai Gao, Yue BNRist THUIBCS BLBCI School of Software Tsinghua University Beijing100084 China THUIBCS Department of Automation Tsinghua University Beijing100084 China BNRist School of Software Tsinghua University Beijing100084 China State Key Laboratory of Human–Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

Existing event-based synthetic aperture imaging (SAI) methods can reconstruct unobstructed images of the background target scene behind dense foreground occlusions from visual information captured by an event camera. However, limited by the spatial resolution of existing event cameras and the computational paradigm of existing event-based SAI methods, the resolution of images reconstructed by existing methods is insufficient. In this paper, we propose a high-resolution (HR) SAI method based on event-frame fusion. Our method trades the high temporal resolution of the event camera for spatial resolution and fuses events and frames to reconstruct the HR unobstructed image. Our proposed method leverages an event-guided occlusion segmentation mechanism to predict the occlusion masks, and extracts multi-view multi-modal valid visual features while invalid parts are discarded. Then, an adaptive fusion module is proposed to align and fuse the multi-view features, and the HR unobstructed scene image is reconstructed from the fused feature. In addition, we construct a multi-sensor vision acquisition system and collect the first Event-based High-Resolution Synthetic Aperture Imaging (THUE-HRSAI) dataset containing low-resolution occluded frames, event streams, and HR ground truth unobstructed scene images, which will be released as the first benchmark. We conduct experiments on our THUE-HRSAI dataset, and the experimental results demonstrate that our proposed method achieves state-of-the-art performance. Downstream application results further prove the necessity of our method. Code and dataset are available at: https://***/lisiqi19971013/E-HRSAI. © 2025 Elsevier B.V.

关键词： Image reconstruction

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Explicitly fusing plug-and-play guidance of source prototype into target subspace for domain adaptation

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Information Fusion 2025年 123卷

作者： Luo, Hao Tian, Zhiqiang Jiao, Panpan Liu, Meiqin Du, Shaoyi Nan, Kai School of Software Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China State Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Joint Surgery Honghui Hospital Xi'an Jiaotong University Shaanxi Xi'an710054 China

The commonly used maximum mean discrepancy (MMD) criterion has two main drawbacks when reducing cross-domain distribution gaps: firstly, it reduces the distribution discrepancy in a global manner, potentially ignoring local structural information between domains, and secondly, its performance heavily relies on the often-unstable pseudo-label refinement process. To solve these problems, we introduce two universal plug-and-play modules: dynamic prototype pursuit (DPP) regularization and bi-branch self-training (BST) mechanism. Firstly, DPP introduces a new inter-class perspective to stabilize MMD by assigning a source prototype to each target sample. This allows us to utilize inter-class data structure information for better alignment. Next, BST is a novel non-parametric pseudo-label refinement mechanism that updates pseudo labels of target data using a classifier trained on the same distribution as the target domain. This avoids the distribution gap issue, making BST more likely to generate accurate target pseudo labels. Importantly, DPP and BST are universal plug-and-play modules for shallow domain adaptation methods. To demonstrate this, experiments of 3 MMD-based models incorporated with DPP and BST are conducted on Office-Caltech, Reuters21578, and Berlin-Emovo-Tess datasets. Experimental results show that these models incorporated with DPP and BST generally achieve better results compared to not using DPP and BST in terms of multiple metrics including accuracy, F1-score, MCC, and false positive rates. Code of 3 different DA methods enhanced by the plug-and-play DPP and BST is available at: https://***/Evelhz/DPP-and-BST. © 2025 Elsevier B.V.

关键词： Bi-branch self-training Dynamic prototype pursuit Shallow domain adaptation Subspace learning

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Cross-Template-Based Hypergraph Transformer

Cross-Template-Based Hypergraph Transformer

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingxi Feng Xiangmin Han Heming Xu Juan Wang Jue Jiang Shaoyi Du Yue Gao National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China School of Software Tsinghua University Beijing China Department of Ultrasound The Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Single-template-based brain functional network analysis methods can provide limited functional connectivity information, which constrains the performance of brain disease diagnosis. Previous works have explored multi-template functional network analysis but failed to integrate the high-order correlation information within templates and the complementary information between templates into a unified relationship strength between nodes, and we extract the high-order correlation information within each template through hypergraph convolution. Secondly, for the analysis of functional connectivity between templates, we propose a cross-template Transformer to capture long-range dependencies between templates. A cross-template mask is applied to focus the model’s attention on important connections between templates, thereby enhancing model robustness. Finally, we progressively fuse the high-order information captured within templates with the global information across templates for downstream classification tasks. The proposed method has been validated on the public ABIDE dataset, and it outperforms existing methods in the ASD diagnosis task.

关键词： Correlation Fuses Convolution Network analyzers Transformers Robustness Acoustics Medical diagnosis Data mining Speech processing

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A Lightweight Model for Perceptual Image Compression via Implicit Priors

arXiv

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arXiv 2025年

作者： Wei, Hao Zhou, Yanhui Jia, Yiwen Ge, Chenyang Anwar, Saeed Mian, Ajmal National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China School of Information and Telecommunication Xi’an Jiaotong University Xi’an710049 China Department of Computer Science and Software Engineering The University of Western Australia Crawley PerthWA6009 Australia

Perceptual image compression has shown strong potential for producing visually appealing results at low bitrates, surpassing classical standards and pixel-wise distortion-oriented neural methods. However, existing methods typically improve compression performance by incorporating explicit semantic priors, such as segmentation maps and textual features, into the encoder or decoder, which increases model complexity by adding parameters and floating-point operations. This limits the model’s practicality, as image compression often occurs on resource-limited mobile devices. To alleviate this problem, we propose a lightweight perceptual Image Compression method using Implicit Semantic Priors (ICISP). We first develop an enhanced visual state space block that exploits local and global spatial dependencies to reduce redundancy. Since different frequency information contributes unequally to compression, we develop a frequency decomposition modulation block to adaptively preserve or reduce the low-frequency and high-frequency information. We establish the above blocks as the main modules of the encoder-decoder, and to further improve the perceptual quality of the reconstructed images, we develop a semantic-informed discriminator that uses implicit semantic priors from a pretrained DINOv2 encoder. Experiments on popular benchmarks show that our method achieves competitive compression performance and has significantly fewer network parameters and floating point operations than the existing state-of-the-art. We will release the code and trained models. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Elementary operation approach to state-space realizations of 2-D systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS 1997年第2期44卷 120-129页

作者： Galkowski, K Department of Robotics and Software Engineering Technical University of Zielona Gora Zielona Gora Poland

In this paper, the method for obtaining of various state-space realizations of the Roesser type for an assigned two-variable transfer function is developed, Two-variable elementary polynomial operations are employed. A new version of the general, state-space model is constructed too, One also throw some new light on the problem of the mutual connections among the different 2-D state-space models as the Roesser, the Fornasini-Marchesini, and the general one. Singular as well as the standard (nonsingular) models are considered.

关键词： multivariable (nD) systems systems theory

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