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检索条件"任意字段=International Conference on Multisensor Fusion and Integration for Intelligent Systems"

共 2572 条记录，以下是41-50 订阅

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Advancements in Image fusion Techniques for IoT systems: Current Trends and Applications 2

Advancements in Image Fusion Techniques for IoT Systems: Cur...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Sharma, Shweta Rani, Shalli Sharma, Ankita Dogra, Ayush Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

ISBN: (纸本)9798331540661;9798331540678

This research explores the transformative potential of integrating image fusion with the Internet of Things (IoT) in the field of smart healthcare. By combining multiple images from various sources, image fusion provides richer and more precise data, enhancing the capabilities of IoT devices. This synergy optimizes data quality and reliability in IoT systems, leading to improved diagnosis, patient monitoring, and telemedicine services in healthcare. The paper investigates the approaches and technologies used in combining image fusion with IoT, addressing challenges and limitations. Through case studies and current implementations, we highlight the transformative potential of this integration in creating smarter, more responsive, and efficient healthcare systems. Additionally, this study explores the implications for other sectors such as environmental monitoring, security, and smart cities.

关键词： Image fusion IoT Remote Sensing GAN ML

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CRPlace: Camera-Radar fusion with BEV Representation for Place Recognition

CRPlace: Camera-Radar Fusion with BEV Representation for Pla...

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2024 international conference on intelligent Robots and systems

作者： Fu, Shaowei Duan, Yilan Li, Yao Meng, Chengzhen Wang, Yingjie Ji, Jianmin Zhang, Yanyong Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

The integration of complementary characteristics from camera and radar data has emerged as an effective approach in 3D object detection. However, such fusion-based methods remain unexplored for place recognition, an equally important task for autonomous systems. Given that place recognition relies on the similarity between a query scene and the corresponding candidate scene, the stationary background of a scene is expected to play a crucial role in the task. As such, current well-designed camera-radar fusion methods for 3D object detection can hardly take effect in place recognition because they mainly focus on dynamic foreground objects. In this paper, a background-attentive camera-radar fusion-based method, named CRPlace, is proposed to generate background-attentive global descriptors from multi-view images and radar point clouds for accurate place recognition. To extract stationary background features effectively, we design an adaptive module that generates the background-attentive mask by utilizing the camera BEV feature and radar dynamic points. With the guidance of a background mask, we devise a bidirectional cross-attention-based spatial fusion strategy to facilitate comprehensive spatial interaction between the background information of the camera BEV feature and the radar BEV feature. As the first camera-radar fusion-based place recognition network, CRPlace has been evaluated thoroughly on the nuScenes dataset. The results show that our algorithm outperforms a variety of baseline methods across a comprehensive set of metrics (recall@1 reaches 91.2%).

关键词： Radar imaging

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Self-Supervised Learning and Multi-Task Pre-Training Based Single-Channel Acoustic Denoising

Self-Supervised Learning and Multi-Task Pre-Training Based S...

引用

IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Li, Yi Sun, Yang Naqvi, Syed Mohsen Newcastle Univ Sch Engn Intelligent Sensing & Commun Grp Newcastle Upon Tyne NE1 7RU England Univ Oxford Big Data Inst Oxford OX3 7LF England

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

In self-supervised learning-based single-channel speech denoising problem, it is challenging to reduce the gap between the denoising performance on the estimated and target speech signals with existed pre-tasks. In this paper, we propose a multi-task pre-training method to improve the speech denoising performance within self-supervised learning. In the proposed pre-training autoencoder (PAE), only a very limited set of unpaired and unseen clean speech signals are required to learn speech latent representations. Meanwhile, to solve the limitation of existing single pre-task, the proposed masking module exploits the dereverberated mask and estimated ratio mask to denoise the mixture as the new pre-task. The downstream task autoencoder (DAE) utilizes unlabeled and unseen reverberant mixtures to generate the estimated mixtures. The DAE is trained to share a latent representation with the clean examples from the learned representation in the PAE. Experimental results on a benchmark dataset demonstrate that the proposed method outperforms the state-of-the-art approaches.

关键词： Noise reduction Self-supervised learning Benchmark testing Multitasking Acoustics Task analysis intelligent systems

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Predicting Autonomous Vehicle Navigation Parameters via Image and Image-and-Point Cloud fusion-based End-to-End Methods

Predicting Autonomous Vehicle Navigation Parameters via Imag...

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IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Beycimen, Semih Ignatyev, Dmitry Zolotas, Argyrios Cranfield Univ Ctr Autonomous & Cyber Phys Syst SATM Bedford MK43 0AL England

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. Image-based and Image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained Image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.

关键词： Training Laser radar Recurrent neural networks Navigation Prediction algorithms Data models Land vehicles

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Arithmetic Average Based Multi-sensor TPHD Filter for Distributed Multi-target Tracking

Arithmetic Average Based Multi-sensor TPHD Filter for Distri...

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IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Fu, Jiazheng Chai, Lei Zhang, Boxiang Yi, Wei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Compared with the probability hypothesis density (PHD) filter for sets of targets, the trajectory probability hypothesis density (TPHD) filter can estimate the sets of trajectories in a principle way and has better target tracking performance. This paper aims at extending the TPHD filter to distributed multi-target tracking (MTT) for the multi-sensor system. However, in the trajectory set based distributed fusion implementation, the trajectory state difference phenomenon makes the clustering and merging techniques unfeasible in trajectory state space. To address this problem, this paper studies the space decomposition of the TPHD and proposes a distributed MTT method based on the TPHD filter with the weighted arithmetic average (WAA) fusion rule. First, we prove the rationality of the space decomposition in the posterior density of the TPHD filter. Then, based on the proposed property, we derive the WAA fusion formulation of the TPHD filter by minimizing the weighted sum of Kullback-Leibler divergences (KLD) from local posterior densities, and develop the analytical Gaussian mixture (GM) implementation with the L-scan approximation. Numerical results demonstrate the efficacy of the proposed fusion method.

关键词： Distributed multi-target tracking trajectory probability hypothesis density filter information fusion weighted arithmetic average

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Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework

Asynchronous Event-Inertial Odometry using a Unified Gaussia...

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2024 international conference on intelligent Robots and systems

作者： Li, Xudong Wang, Zhixiang Liu, Zihao Zhang, Yizhai Zhang, Fan Yao, Xiuming Huang, Panfeng Northwestern Polytech Univ Shaanxi Prov Innovat Team Intelligent Robot Techn Xian Peoples R China Northwestern Polytech Univ Sch Automat Shaanxi Prov Innovat Team Intelligent Robot Techn Xian Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Recent works have combined monocular event camera and inertial measurement unit to estimate the SE(3) trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme.

关键词： Gaussian distribution

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Noninformative Prior Weights for Dirichlet PDFs

Noninformative Prior Weights for Dirichlet PDFs

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IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Josang, Audun Cho, Jin-Hee Chen, Feng Univ Oslo Oslo Norway Virginia Tech Blacksburg VA USA Univ Texas Dallas Dallas TX USA

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

The noninformative prior weight W of a Dirichlet PDF (Probability Density Function) determines the balance between the prior probability and the influence of new observations on the posterior probability distribution. In this work, we propose a method for dynamically converging the weight W in a way that satisfies two constraints. The first constraint is that the prior Dirichlet PDF (i.e. in the absence of evidence) must always be uniform, which dictates that W = k where k is the cardinality of the domain. The second constraint is that the prior weight of large domains must not be so heavy that it prevents new observation evidence from having the expected influence over the shape of the Dirichlet PDF, which dictates that W quickly converges to a low constant C-W in the presence of observation evidence, where typically C-W = 2. In the case of a binary domain, the noninformative prior weight is normally set to W = 2, irrespective of the amount of evidence. In the case of a multidimensional domain with arbitrarily large cardinality k, the noninformative prior weight is initially equal to the domain cardinality k, but rapidly decreases to the constant convergence factor C-W as the amount of evidence increases.

关键词： Shape Probability density function Probability distribution intelligent systems Convergence

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A Review of Visual SLAM systems Based on Multi-Sensor fusion 9

A Review of Visual SLAM Systems Based on Multi-Sensor Fusion

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9th international conference on intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Liu, Yunxiang Tan, Yi Shanghai Institute of Technology Sit Shanghai China

ISBN: (纸本)9798350363043

In the realm of autonomous systems, the precision and robustness of Simultaneous Localization and Mapping (SLAM) are significantly bolstered through the integration of diverse sensor modalities. This review explores the critical role of multi-sensor fusion in visual SLAM, which amalgamates data from Inertial Measurement Units (IMUs), visual cameras, LiDAR, and more. We commence with an overview of the foundational principles of visual SLAM and trace its evolutionary trajectory. Subsequently, we dissect the core tenets of multi-sensor fusion, elucidating the types of sensors involved and the strategies and algorithms employed for their synergistic integration. As we traverse through the landscape of current research, we identify and discuss the emerging trends and future horizons of this dynamic field. The review culminates in an analysis of the challenges inherent to multi-sensor fusion systems, proposing innovative solutions to surmount these obstacles and set the stage for future advancements. © 2024 IEEE.

关键词： Autonomous Driving Multi-Sensor fusion Sensor Data fusion Visual SLAM

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Anytimefusion: Parameter-free RGB Camera-Radar Sensor fusion Algorithm in Complex Maritime Situations

AnytimeFusion: Parameter-free RGB Camera-Radar Sensor Fusion...

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2024 international conference on intelligent Robots and systems

作者： Shin, Yeongha Kim, Hanguen Kim, Jinwhan Seadronix Corp Ulsan South Korea Korea Adv Inst Sci & Technol Robot Program Daejeon 34141 South Korea CTO Seadronix Corp Seoul 06235 South Korea Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea

ISBN: (纸本)9798350377712;9798350377705

Determining the position of obstacles is crucial for unmanned vehicles, and, to achieve this, cameras and radar sensors are widely utilized. However, establishing correlation between two or more sensors proves challenging in the dynamically changing maritime environment. To solve these issues, we propose the Anytimefusion algorithm. The key innovation of Anytimefusion lies in the utilization of a parameter-free method that does not require accurate sensor alignment and calibration. The algorithm consists of the following four stages. First, calibration targets are selected in the maritime environment based on segmentation images. Second, radar and camera data are pre-fused to model the correlation of azimuth information. After completing the auto-calibration stages, Inverse Perspective Mapping (IPM) is employed to integrate the coordinate systems of the two sensors. To determine the parameters for this integration, optimization based on the Particle Swarm Optimization (PSO) method is employed. Finally, an Error Polygon for the positions of the camera and radar is generated, and sensor fusion is carried out based on this information. We validated our method through experiments conducted on real ships in complex maritime environments, achieving an average accuracy of 95.7%.

关键词： Particle swarm optimization (PSO)

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2024 IEEE 27th international conference on intelligent Transportation systems, ITSC 2024

2024 IEEE 27th International Conference on Intelligent Trans...

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27th IEEE international conference on intelligent Transportation systems, ITSC 2024

ISBN: (纸本)9798331505929

The proceedings contain 627 papers. The topics discussed include: connected vehicle-enabled centralized routing with dynamic network allocation;a novel cooperative driving mechanism based on multi-agent deep reinforcement learning with priority parameter sharing and safety rules;charging infrastructure planning for electrifying the lake Michigan circuit under various budget and feasibility constraints;hybrid energy-efficient local path planning for autonomous vehicles in dynamic environments;CCDepth: a lightweight self-supervised depth estimation network with enhanced interpretability;robust localization for autonomous vehicles via multisensor fusion;impact of perspectives on collective perception;and the extraction and generation of complex critical scenarios for demonstration area-oriented C-V2X test.

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