2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第2页

Distributed optimal fusion estimation for multi-sensor systems subject to random delay and packet drop 随机时延和丢包多传感器系统的分布式最优融合估计

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343075

Jiabing Sun, Chengjin Zhang

{"title":"Distributed optimal fusion estimation for multi-sensor systems subject to random delay and packet drop","authors":"Jiabing Sun, Chengjin Zhang","doi":"10.1109/MFI.2012.6343075","DOIUrl":"https://doi.org/10.1109/MFI.2012.6343075","url":null,"abstract":"This paper considers the distributed optimal (i.e., linear minimum variance) fusion estimation problems for two classes of networked multi-sensor systems. In the first class of systems, the sensors' measurements are transmitted via unreliable digital communication networks (DCN) to local estimators for obtaining local estimates of the state. Then the local estimates are fused in the fusion center to get the fusion estimate. The data transmission from local estimators to the fusion center is not via DCN. In the second class of systems, the sensors' measurements are processed locally to obtain local estimates of the state. Then, via DCN, the local estimates are transmitted to the fusion center where they are fused to get the fusion estimate. In these systems, the data transmission via DCN is subject to random delay and packet drop. The results of local estimation have been presented in the literature. The estimation error cross-covariances between local estimates are derived in this paper. By the fusion rule weighted by matrices, the distributed optimal fusion estimators are developed.","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130904341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improving robustness of robotic grasping by fusing multi-sensor 多传感器融合提高机器人抓取鲁棒性

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343002

Jun Zhang, Caixia Song, Ying Hu, Bin Yu

{"title":"Improving robustness of robotic grasping by fusing multi-sensor","authors":"Jun Zhang, Caixia Song, Ying Hu, Bin Yu","doi":"10.1109/MFI.2012.6343002","DOIUrl":"https://doi.org/10.1109/MFI.2012.6343002","url":null,"abstract":"Since the visual system is susceptible to the lighting condition and surroundings changes, the accuracy for object localization of robot grasping system based on visual servo is rather poor so as to the low grasping success rate and bad robustness of the whole system. In view of such phenomenon, in this paper, we propose a method of fusing binocular camera accompany with monocular vision, IR sensors, tactile sensors and encoders to design a reliable and robust grasping system that could offer real-time feedback information. In order to avoid the situation of robot grasping-nothing, we use the binocular vision supplemented by monocular camera and IR sensors to locate accurately. By analyzing the contact model and pressure between gripper and the object, a durable, non-slip rubber coating is designed to increase the fingertip's friction, What's more, Fuzzy Neural Network (FNN) method was applied to fuse the information of multiple sensors in our robot system. By monitoring force and position information in the process of grasping all the time, the system can reduce the phenomenon of slippage and crush of object as well as improve the grasping stability greatly. The experimental results show the effectiveness of our system.","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122324121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Sensor fault detection for industrial systems using a hierarchical clustering-based graphical user interface 基于分层聚类的图形用户界面的工业系统传感器故障检测

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343071

Yu Zhang, C. Bingham, M. Gallimore, Zhijing Yang, Jun Chen

引用次数: 4

Estimation of occupant distribution by detecting the entrance and leaving events of zones in building 通过检测建筑物各区域的进出事件来估计人员分布

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343074

Hengtao Wang, Q. Jia, Yu Lei, Qianchuan Zhao, X. Guan

引用次数: 10

Robust visual tracking based on adaptive depth-color-cue integration using range sensor 基于深度-颜色自适应线索融合的距离传感器鲁棒视觉跟踪

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343012

Can Wang, Hong Liu

引用次数: 5

Space encoding based compressive multiple human tracking with distributed binary pyroelectric infrared sensor networks 基于空间编码的分布式二值热释电红外传感器网络压缩多人跟踪

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6342997

Jiang Lu, Jiaqi Gong, Qi Hao, Fei Hu

引用次数: 26

A sensing cushion using simple pressure distribution sensors 使用简单压力分布传感器的传感垫

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343048

Lishuang Xu, Gang Chen, Jiajun Wang, R. Shen, Shen Zhao

引用次数: 25

Map segmentation based SLAM using embodied data 基于嵌入数据的SLAM地图分割

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343018

J. Schwendner

引用次数: 0

Study on impedance generation using an exoskeleton device for upper-limb rehabilitation 上肢康复外骨骼装置阻抗产生的研究

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343006

Zhibin Song, Shuxiang Guo

{"title":"Study on impedance generation using an exoskeleton device for upper-limb rehabilitation","authors":"Zhibin Song, Shuxiang Guo","doi":"10.1109/MFI.2012.6343006","DOIUrl":"https://doi.org/10.1109/MFI.2012.6343006","url":null,"abstract":"Rehabilitation robotics has been one of the most important branches of robotics. Particularly, the exoskeleton device for the upper limb rehabilitation develops fast in recent years, but most of them are heavy and large. In this paper, we proposed a light and wearable exoskeleton device which is potential to be used in home rehabilitation. It should be able to perform passive and active training. In this paper, we proposed a method to implement the active rehabilitation based on the upper limb exoskeleton rehabilitation device (ULERD). It provides a wide approach for human machine interface (HMI) in which the device is high friction and non-backdrivable, and meanwhile it is difficult to obtain the contact force information directly. The method is to measure the motion of human body other than the motion of device. It is implemented with passive DoFs unlocked during elbow flexion and extension performance. In contrast experiments, three level resistances are generated and provided to the user. The surface electromyography (sEMG) signals detected from biceps and triceps were recoded and processed to evaluate the effect of this method via wavelet packet transform (WPT).","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123919983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Tumbling target reconstruction and pose estimation through fusion of monocular vision and sparse-pattern range data 基于单目视觉与稀疏模式距离数据融合的翻滚目标重建与位姿估计

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2012-11-12 DOI: 10.1109/MFI.2012.6343026

J. Padial, M. Hammond, S. Augenstein, S. Rock

{"title":"Tumbling target reconstruction and pose estimation through fusion of monocular vision and sparse-pattern range data","authors":"J. Padial, M. Hammond, S. Augenstein, S. Rock","doi":"10.1109/MFI.2012.6343026","DOIUrl":"https://doi.org/10.1109/MFI.2012.6343026","url":null,"abstract":"A framework for 3D target reconstruction and relative pose estimation through fusion of vision and sparse-pattern range data (e.g. line-scanning LIDAR) is presented. The algorithm augments previous work in monocular vision-only SLAM/SfM to incorporate range data into the overall solution. The aim of this work is to enable a more dense reconstruction with accurate relative pose estimation that is unambiguous in scale. In order to incorporate range data, a linear estimator is presented to estimate the overall scale factor using vision-range correspondence. A motivating mission is the use of resource-constrained micro- and nano-satellites to perform autonomous rendezvous and docking operations with uncommunicative, tumbling targets, about which little or no prior information is available. The rationale for the approach is explained, and an algorithm is presented. The implementation using a modified Rao-Blackwellised particle filter is described and tested. Results from numerical simulations are presented that demonstrate the performance and viability of the approach.","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115156780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12