多模态信息融合

YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors

Kamil Kowol, Matthias Rottmann, Stefan Bracke, Hanno Gottschalk

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.CV

Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles

Ramin Nabati, Hairong Qi

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.CV

Comments 12th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, IROS 2020

Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding

Zhiyu Huang, Chen Lv, Yang Xing, Jingda Wu

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.RO

Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network

Anh Nguyen, Ngoc Nguyen, Kim Tran, Erman Tjiputra, Quang D. Tran

专题命中 机器人多传感器融合 ：multimodal fusion(title,abstract)；分类 cs.RO

Comments Accepted to IROS 2020

Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles

Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.CV

Comments Accepted by the 31st IEEE Intelligent Vehicles Symposium

Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data

Tom Van Steenkiste, Dirk Deschrijver, Tom Dhaene

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 eess.SP

Comments Paper presented at ML4H (Machine Learning for Health) workshop at NeurIPS 2019. https://ml4health.github.io/2019/

Sensor Fusion: Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data

Athma Narayanan, Avinash Siravuru, Behzad Dariush

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.CV

Comments Accepted to Robotics and Automation Letters 2020

Bi-objective Framework for Sensor Fusion in RGB-D Multi-View Systems: Applications in Calibration

Hassan Afzal, Djamila Aouada, Michel Antunes, David Fofi, Bruno Mirbach, Björn Ottersten

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.CV

Comments 23 pages

LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

Bernie Wang, Virginia Wu, Bichen Wu, Kurt Keutzer

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.CV

Sensor Fusion for Predictive Control of Human-Prosthesis-Environment Dynamics in Assistive Walking: A Survey

Kuangen Zhang, Clarence W. de Silva, Chenglong Fu

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.RO

Non-Coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport

Filip Elvander, Isabel Haasler, Andreas Jakobsson, Johan Karlsson

专题命中 机器人多传感器融合 ：sensor fusion(title)；information fusion(abstract)；分类 eess.SP

Multi-Sensor Conflict Measurement and Information Fusion

Pan Wei, John E. Ball, Derek T. Anderson

专题命中 机器人多传感器融合 ：information fusion(title)；sensor fusion(abstract)；分类 eess.SP

Comments 15 pages, 9 figures, conference paper

Journal ref SPIE Defense, Security, and Sensing, April, 2016

Statistical Sensor Fusion of a 9-DoF MEMS IMU for Indoor Navigation

Jacky C. K. Chow

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 eess.SP

Journal ref Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 333-338, 2017

Wrist Sensor Fusion Enables Robust Gait Quantification Across Walking Scenarios

Zeev Waks, Itzik Mazeh, Chen Admati, Michal Afek, Yonatan Dolan, Avishai Wagner

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 eess.SP

Comments NIPS 2017 Machine Learning for Health Workshop

Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data

Mehmet Turan, Yasin Almalioglu, Hunter Gilbert, Alp Eren Sari, Ufuk Soylu, Metin Sitti

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.RO

Comments Submitted to ICRA 2018

Robust Inference for Visual-Inertial Sensor Fusion

Konstantine Tsotsos, Alessandro Chiuso, Stefano Soatto

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)；分类 cs.RO

Comments Submitted to ICRA 2015, Manuscript #2912. Video results available at: http://youtu.be/5JSF0-DbIRc

Deep Multimodal Wearable Sensor Fusion for Detection of Body-Focused Repetitive Behaviors

用于聚焦身体重复行为检测的深度多模态可穿戴传感器融合

Samaneh Rezaeimanesh, Mohsen Behradfar, Mohammad Fili, Guiping Hu

机构 * George Mason University（乔治梅森大学） 纠错

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 本研究开发多模态深度学习框架，利用腕戴式传感器数据检测聚焦身体的重复行为，在二元及九分类任务中均优于单模态基线，为可穿戴辅助心理健康诊断奠定基础。

Event-triggered parameter estimator for sensor fusion

传感器融合的事件触发参数估计器

Ariana Méndez-Castillo, Irene Perez-Salesa, Rodrigo Aldana-López, Antonio Ramírez-Treviño, Rosario Aragues

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 研究传感器融合系统中事件触发参数估计，引入回归器驱动局部触发规则，在持续激励条件下推导出保证全局指数收敛的设计不等式，基于时变李雅普诺夫函数分析，仿真显示节省通信并保持收敛性。

Comments This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND. Accepted for presentation at the 23rd IFAC World Congress 2026

A Multi-Modal Sensor Fusion Instrument for Measuring Regional Human Mobility: The Distributed Human Data Engine (DHDE)

多模态传感器融合仪器用于测量区域人类流动性：分布式人类数据引擎（DHDE）

Amil Khanzada, Takuji Takemoto

机构 * Headquarters for Regional Revitalization, University of Fukui, Japan（复兴地区总部，福井大学，日本） 纠错

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 提出分布式人类数据引擎（DHDE），通过融合边缘AI相机、数字意图信号、行为记录和气象数据，解决外围区域人类流动性测量中传感器稀疏和行为异质性问题，验证了稀疏传感器补偿方法，并发现“低活力悖论”。

Comments 32 pages, 4 figures, 3 tables. Pre-print of a manuscript submitted for peer review (v2)

Bias Estimation for Decentralized Sensor Fusion -- Multi-Agent Based Bias Estimation Method

去中心化传感器融合中的偏差估计——基于多智能体的偏差估计方法

Hidetoshi Furukawa

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 本文提出基于多智能体模型的新型偏差估计方法，用于去中心化传感器融合中的偏差估计与补偿，解决传统方法无法处理各传感器节点跟踪数据融合的问题。

Comments Technical Report, 6 pages, in Japanese, Copyright(C)2017 IEICE

Journal ref IEICE Technical Report, vol.117, no.43, SANE2017-7, pp.35-40, May 2017

Achieving Sensor Fusion for Collaborative Multi-level Monitoring of Pipeline Infrastructures

实现管道基础设施协同多级监测的传感器融合

Grigore Stamatescu

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 本文提出多级系统架构，结合无线传感器网络和无人机，通过三种传感器融合方法提升数据采集与处理效率，缓解多级网络通信瓶颈。

Comments 12 pages, 6 figures

On sensor fusion for airborne wind energy systems

关于机载风能系统的传感器融合

Lorenzo Fagiano, Khahn Huynh, Bassam Bamieh, Mustafa Khammash

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 针对机载风能发电机的滤波问题，通过将非线性系统分解为子系统，提出基于运动学定律的传感器融合算法，实现翼体位置、速度和航向的精确重构。

Comments This manuscript is a preprint of a paper accepted for publication on the IEEE Transactions on Control Systems Technology and is subject to IEEE Copyright. The copy of record is available at IEEEXplore library: http://ieeexplore.ieee.org/

DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance

DeepIPCv3: 面向突发行人穿越避让的事件感知多模态传感器融合

Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky, Jazi Eko Istiyanto, Jun Miura

机构 * Department of Computer Science and Electronics, Universitas Gadjah Mada（计算机科学与电子系，加雅马达大学） ； Department of Computer Science and Engineering, Toyohashi University of Technology（计算机科学与工程系，东福士大学） 纠错

专题命中 机器人多传感器融合 ：sensor fusion(title)；分类 cs.CV、eess.IV、cs.RO

AI总结 提出DeepIPCv3框架，通过Transformer交叉模态注意力融合LiDAR点云与DVS事件流，实现突发行人穿越场景下的高反应性避让，在自定义多模态数据集上达到最优轨迹与控制精度。

Quantum-Enhanced Distributed Sensor Fusion: Lower Bounds on Aggregation from Projection Noise to Heisenberg-Limited Byzantine-Tolerant Networks

量子增强的分布式传感器融合：从投影噪声到海森堡极限的容错网络下限

Vasanth Iyer, S. S. Iyengar

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 本文研究了在拜占庭故障和退相干条件下分布式量子传感器融合的均方误差下限，通过结合经典Brooks-Iyengar重叠函数、预测离群模型和SPOTLESS时空验证，建立了由纠缠可见度V和故障比例f/M参数化的双参数下限，验证了理论缩放规律并展示了量子计量与经典流处理架构的桥梁作用。

Comments 14 pages, 8 figures, 2 tables, code at https://github.com/viyer-research/quantum-sensor-fusion

Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting

视线外的具身智能体：多模态跟踪、传感器融合与轨迹预测

Haichao Zhang, Yi Xu, Yun Fu

机构 * Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系） ； Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院） 纠错

专题命中 机器人多传感器融合 ：sensor fusion(title)；分类 cs.CV、cs.RO、cs.MM

AI总结 本文提出改进的视线外轨迹预测方法，通过视觉定位去噪模块提升轨迹预测性能，实验表明在Vi-Fi和JRDB数据集上达到最优效果，为自动驾驶、机器人和监控提供新方向。

Comments Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (Early Access), pp. 1-14, March 23, 2026

Journal ref IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

AI-Driven Real-Time Kick Classification in Olympic Taekwondo Using Sensor Fusion

基于AI的奥运跆拳道实时踢击分类技术：传感器融合应用

Jamsheed Mistri

机构 * University of California, Berkeley（加州大学伯克利分校） 纠错

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

AI总结 本文提出基于AI和传感器融合的实时踢击分类系统，通过改进评分标准提升跆拳道比赛的动态性和观赏性。

Comments 13 pages, 4 figures

Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing

Chiara De Luca, Elisa Donati

机构 * Institute of Neuroinformatics University of Zurich and ETH Zurich（神经信息学研究所（苏黎世大学和苏黎世联邦理工学院）） ； Digital Society Initiative University of Zurich（数字社会倡议（苏黎世大学）） 纠错

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

Sensor fusion luminescence thermometry better together

Aleksandar Ćirić, Zoran Ristić, Tamara Gavrilović, Jovana Periša, Mina Medić, Miroslav D. Dramićanin

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

Comments 18 pages, 9 figures

Journal ref Laser Photonics Rev 2025, e00781

Security-Aware Sensor Fusion with MATE: the Multi-Agent Trust Estimator

R. Spencer Hallyburton, Miroslav Pajic

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

Deep learning empowered sensor fusion boosts infant movement classification

Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B Marschik

专题命中 机器人多传感器融合 ：sensor fusion(title,abstract)

Journal ref Communications Medicine, 2025