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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications"

共 2091 条记录，以下是1-10 订阅

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Robust Long-Range Perception Against sensor Misalignment in Autonomous Vehicles

Robust Long-Range Perception Against Sensor Misalignment in ...

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2025 IEEE/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Xia, Zi-Xiang Fadadu, Sudeep Shi, Yi Foucard, Louis Aurora Innovation Inc. United States

ISBN: (纸本)9798331510831

Advances in machine learning algorithms for sensor fusion have significantly improved the detection and prediction of other road users, thereby enhancing safety. However, even a small angular displacement in the sensor's placement can cause significant degradation in output, especially at long range. In this paper, we demonstrate a simple yet generic and efficient multi-task learning approach that not only detects misalignment between different sensor modalities but is also robust against them for long-range perception. Along with the amount of misalignment, our method also predicts calibrated uncertainty, which can be useful for filtering and fusing predicted misalignment values over time. In addition, we show that the predicted misalignment parameters can be used for self-correcting input sensor data, further improving the perception performance under sensor misalignment. © 2025 IEEE.

关键词： Object detection

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Research on the fusion of Autonomous Navigation algorithms and Environmental Perception in Intelligent Ship Technology 5

Research on the Fusion of Autonomous Navigation Algorithms a...

引用

5th IEEE International conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Zhang, Zhijie Zong, Yonggang Binzhou Polytechnic Ocean Department Binzhou China

ISBN: (纸本)9798331533694

The research focus of this paper is on the organic combination of autonomous navigation algorithm and environmental perception technology, with the aim of enhancing the autonomous navigation capability of intelligent ships in complex marine environments. Autonomous navigation relies on the close coordination of multiple perception systems and algorithms, among which the simultaneous localization and mapping (SLAM) algorithm is particularly critical. The SLAM algorithm generates high-precision environmental maps in real time and provides accurate ship positioning by fusing multi-sensor information. This paper proposes and designs an autonomous navigation system that integrates environmental perception and SLAM technology. Its system architecture includes sensor data processing, path planning and control decision modules. The effectiveness of the proposed algorithm is verified through a series of model simulations, and experimental evaluation is carried out in multiple test scenarios. The simulation results show that compared with traditional navigation methods, the algorithm significantly improves the navigation accuracy and safety of ships in complex marine environments, and the positioning error rate is reduced by more than 15%. The research results of this paper provide a solid theoretical basis and technical support for the future research and development of autonomous navigation systems for intelligent ships. © 2025 IEEE.

关键词： Marine navigation

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sensor fusion Techniques for Accurate Indoor Tracking in IoT-Based Smart Environments 3

Sensor Fusion Techniques for Accurate Indoor Tracking in IoT...

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3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Krishnakumar, S. Acharjya, Kalyan Khurana, Shivam Swain, Rakesh Ranjan Rampal, Sourav Manjunath, C. Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

sensor fusion integrates data from multiple sensors to provide a more accurate, reliable, and contextual sense of the surrounding environment. So, it became a key method for correct monitoring in battery-dependent Internet of Things clever environments, especially Indoor tracking. There is only one dedicated sensor. To benefit the most from it and surround these weaknesses in complex indoor environments with wrongness. The sensor fusion methods for accurate indoor tracking in IoT-based Smart Environments are proposed to solve the problems above by integrating data from multiple sources, including Wi-Fi APs and BLE beacons, RFID readers, and IMUs. This results in a more complete and detailed view of the environment crucial for location-based applications such as asset tracking, localization, or navigation within smart buildings. There are many algorithms to fuse sensor data. The algorithms process the sensor data and give a fused result that outperforms all of these individual components. sensor fusion in IoT-based smart environments improves indoor tracking accuracy, robustness, and scalability. It also permits real-time tracking localization of objects and people;it is the key to optimally managing smart environments. The sensor fusion method generally improves indoor tracking properties in the rapidly growing IoT-based smart environments. © 2025 IEEE.

关键词： Accurate applications Environment Integrates Properties

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DBIF: Dual-Branch Feature Extraction Network for Infrared and Visible Image fusion 7th

DBIF: Dual-Branch Feature Extraction Network for Infrared an...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Zhang, Haozhe Cui, Rongpu Zheng, Zhuohang Gao, Shaobing Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9789819786848;9789819786855

In image fusion, combining infrared and visible images from different sensors is crucial to create a complete representation that merges complementary information. However, current deep learning approaches, mainly using Convolutional Neural Networks (CNNs) or Transformer architectures, do not fully capitalize on the distinct features of infrared and visible images. To overcome this limitation, we introduce a novel Dual-Branch feature extraction network for infrared and visible image fusion (DBIF). DBIF optimally leverages the advantages of CNN and Transformer for feature extraction from different types of images. Specifically, the Transformer's proficiency in extracting global features renders it more suitable for extracting target information from infrared images, while the CNN's superior sensitivity to capturing local information makes it more adept at extracting background texture information from visible images. Consequently, our DBIF architecture incorporates two distinct branches, content and detail, for feature extraction from infrared and visible images, respectively. Additionally, we introduce a Detailed Feature Enhancement Module (DFEM) to consolidate and amplify the prominent features extracted by the detailed branch. Through extensive experimentation across multiple datasets, we validate the effectiveness of our proposed approach, showcasing its superiority over existing fusion algorithms. Furthermore, our method shows substantial performance improvements, especially in object detection tasks. This underscores its practical relevance in various real-world applications that require accurate and efficient fusion of diverse image data types.

关键词： Image fusion CNN Transformer Object Detection

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Three-Axis Attitude Determination of a Nanosatellite Using Optimized TRIAD algorithms 2nd

Three-Axis Attitude Determination of a Nanosatellite Using O...

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2nd International conference on Information Technologies and Their applications

作者： Kirci, Orhan Hajiyev, Chingiz Istanbul Tech Univ Fac Aeronaut & Astronaut TR-34469 Istanbul Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper aims to develop a comprehensive approach for determining the attitude of a nanosatellite. Accurate determination of these parameters is essential for the proper operation of the nanosatellite and its successful mission completion. To achieve this goal, a detailed simulation of the magnetometer and sun sensor measurements in the body and orbital frame of the nanosatellite is conducted. These measurements are critical for determining the attitude of the nanosatellite and ensuring that it is pointing in the right direction. The attitude determination process is carried out using the Three-Axis Attitude Determination (TRIAD) algorithm, which is a popular and effective approach for estimating the attitude of a nanosatellite. The optimization of TRIAD with three different approaches is analyzed to ensure high accuracy and efficiency in determining the attitude of the nanosatellite. A covariance analysis is performed to evaluate the accuracy and reliability of the attitude determination process. The analysis provides valuable insights into the error sources and uncertainties associated with the measurements and estimation process. This information is used to improve the performance of the system and enhance the accuracy of the results. Overall, the results of this paper are expected to contribute significantly to the development of efficient and accurate methods for determining the attitude of a nanosatellite.

关键词： nanosatellite attitude determination Euler angles optimized TRIAD data fusion sun sensor magnetometer

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Multi-modal Cooperative Perception of Constrained Multi-UAV Platform 17th

Multi-modal Cooperative Perception of Constrained Multi-UAV ...

引用

17th International conference on Intelligent Robotics and applications

作者： Zhang, Yiheng Cui, Yuanzhe Su, Yongbo Li, Tong Wang, Bingzheng Tang, Qirong Tongji Univ Lab Robot & Multibody Syst Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9789819607884;9789819607891

The constrained multi-UAV platform is composed of multiple unmanned aerial vehicles (UAVs) connected physically and mutually. When multiple UAVs operate collaboratively on an interconnected platform, challenges in external environment perception arise due to limited movement range and sensor disturbance among UAVs. Additionally, real-time perception is crucial for various UAV tasks, posing a significant challenge for conventional single-modal algorithms to balance speed and accuracy effectively. This paper proposes an innovative multi-modal collaborative sensing method for multi-UAV systems, integrating point cloud and image processing algorithms. A feature-level fusion is employed by our framework to accurately determine target positioning in real-time. The multi-UAV decision layer utilizes a Kalman Filter to extract data from multiple individual outputs, yielding stable and accurate positioning and classification information. Simulation experiments validate the robustness of this method, reducing disturbance from structural and mutual obstructions among UAVs, and providing accurate target positioning.

关键词： Multi-UAV Perception sensor fusion Target Detection

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Cluster Head Selection Algorithm using Machine Learning 4

Cluster Head Selection Algorithm using Machine Learning

引用

4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nidhya, M.S. Gurumoorthi, E. Babu, M. Charumathi, A.C. Naved, Mohd Maram, Balajee School of Computer Applications Dayananda Sagar University Bangalore India School of Engineering Malla Reddy University India Chennai Institute of Technology College Dept of Ai & Ds Chennai India Sona College of Technology Dept of Information Technology India Jaipuria Institute of Management Dept of Business Analytics Noida India School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Warangal India

ISBN: (纸本)9798331523923

Wireless sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. The conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. This research discusses Machine Learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. The comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. The research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies. © 2025 IEEE.

关键词： Self-supervised learning

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MARL-DCF: A Dual-Channel fusion Routing Algorithm Based on Multi-agent Reinforcement Learning 24th

MARL-DCF: A Dual-Channel Fusion Routing Algorithm Based on ...

引用

24th International conference on algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Li, Zhenyu Sun, Runyuan Liang, Zhifeng Liu, Bo Sun, Zhenxiang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9789819615414

Emerging applications have imposed stringent demands on Internet quality of service (QoS). To ensure that various data streams within different Internet services receive the appropriate QoS, advancements in flow classification technology, software-defined networking (SDN), and programmable network devices have enabled the network to identify user demands and precisely control fine-grained traffic routing quickly. Although significant progress has been made with these technologies, optimizing the transmission paths of business flows to meet diverse QoS requirements in real-time remains a challenge. To address this issue, this paper proposes a dual-channel fusion routing algorithm based on multi-agent reinforcement learning (MARL-DCF). Implemented within an SDN architecture, the algorithm was experimentally validated in a Mininet environment using real network topologies and traffic trajectories. The results demonstrate that MARL-DCF can optimize routing paths according to the network environment, meet various QoS demands, and exhibit strong adaptability and reliability in complex scenarios such as link failures and traffic fluctuations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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A Flexible sensor Based on PVC Gel for Detections of Robotic Grasping 17th

A Flexible Sensor Based on PVC Gel for Detections of Robotic...

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17th International conference on Intelligent Robotics and applications

作者： Zhong, Qiyun He, Qingsong Xie, Lin Shi, Ziyan Nanjing Univ Aeronaut & Astronaut Jiangsu Prov Key Lab Bion Funct Mat Coll Mech & Elect Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Adv Technol Small & Medium Sized UAV Minist Ind & Informat Technol Unmanned Aerial Vehicles Res Inst Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China

ISBN: (纸本)9789819607945;9789819607952

The rapid evolution of the smart industry has propelled significant advancements in both the realms of flexible electronics and robotics. The fusion of flexible sensing with intelligent robots bestows upon them the capability of tactile sensing, a critical enhancement that elevates their intelligence, digitalization, and practical applicability. We have triumphantly developed an innovative poly(vinyl chloride)/multi-walled carbon nanotubes polymer gel (PVC/MWCNTs polymer gel, PMPG), renowned for its exceptional elasticity and sensitivity. This high-performing sensing gel was crafted through a physical cross-linking method, utilizing poly(vinyl chloride) as the base matrix and multi-walled carbon nanotubes as the reinforcing fillers. Optimally, at a PVC:DBA = 1:5 (1 wt% of MWCNTs), PMPG demonstrates a remarkable response time of merely 172 ms, coupled with the capacity for sustained, continuous measurements. The material's stability is commendable, enduring up to 2,500 successive measurements without degradation. By integrating PMPG with machine learning algorithms, the soft gripper is endowed with enhanced tactile sensing and responsive feedback capabilities, heralding a new era for soft robotics across diverse settings.

关键词： Flexible sensor PMPG Tactile Sensing

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Research on the Application of Multimodal Sensing Technology in Surveying and Mapping Data Collection

Research on the Application of Multimodal Sensing Technology...

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Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Liu Bo Weng Jianying School of Architectural Engineering Shandong University of Engineering and Vocational Technology Jinan Shandong China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper proposes an innovative CNN and RNN fusion algorithm to optimize the application of multimodal sensing technology in surveying and mapping data collection. The algorithm combines the data of multiple sensors such as lidar, RGB camera and infrared sensor, uses CNN to extract the spatial features of the image, and uses RNN to analyze the temporal relationship between different modal data, so as to achieve efficient fusion of multimodal data. Experimental results show that the proposed algorithm improves the data accuracy by about 18% in multiple surveying and mapping scenarios compared with the traditional single-modal sensor method, and the processing efficiency is improved by 15%. In addition, the algorithm shows significant robustness in complex environments, can effectively cope with the challenges brought by environmental changes, and improves the accuracy and stability of data. The research in this paper provides technical support for the development of intelligent surveying and mapping systems and promotes the popularization of multimodal data fusion algorithms in practical applications.

关键词： Deep learning Solid modeling Accuracy Multimodal sensors Data integration Data collection sensor fusion Feature extraction Stability analysis Thermal stability

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