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2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献

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Robot programming by demonstration of multiple tasks within a common environment 机器人编程通过演示多个任务在一个共同的环境
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170389
Tohid Alizadeh, Batyrkhan Saduanov
{"title":"Robot programming by demonstration of multiple tasks within a common environment","authors":"Tohid Alizadeh, Batyrkhan Saduanov","doi":"10.1109/MFI.2017.8170389","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170389","url":null,"abstract":"Most of the available robot programming by demonstration (PbD) approaches focus on learning a single task, in a given environmental situation. In this paper, we propose to learn multiple tasks together, within a common environment, using one of the available PbD approaches. Task-parameterized Gaussian mixture model (TP-GMM) is used at the core of the proposed approach. A database of TP-GMMs will be constructed for the tasks, and it will be used to provide the reproduction when needed. The environment will be shared between different tasks, in other words, all the available objects will be considered as external task parameters (TPs), as they may modulate the task. During the learning part, the relevance of the task parameters will be extracted for each task, and the information will be stored together with the parameters of the corresponding updated TP-GMM. For reproduction, the end user will specify the task and the robot will be able to pick the relevant TP-GMM and the relevant task parameters and reproduce the movement. The proposed approach is tested both in simulation and using a robotic experiment.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124542091","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: 3
Filtering on the unit sphere using spherical harmonics 利用球谐对单位球进行滤波
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170417
F. Pfaff, G. Kurz, U. Hanebeck
{"title":"Filtering on the unit sphere using spherical harmonics","authors":"F. Pfaff, G. Kurz, U. Hanebeck","doi":"10.1109/MFI.2017.8170417","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170417","url":null,"abstract":"For manifolds with topologies that strongly differ from the standard topology of Rn, using common filters created for linear domains can yield misleading results. While there is a lot of ongoing research on estimation on the unit circle, higher-dimensional problems particularly pose a challenge. One important generalization of the unit circle is the unit hypersphere. In this paper, we propose a recursive Bayesian estimator for the unit sphere S2 based on spherical harmonics for arbitrary likelihood functions and rotationally symmetric system noises. In our evaluation, the proposed filter outperforms the particle filter in a target tracking scenario on the sphere.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127596453","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: 3
Global Point-to-hyperplane ICP: Local and global pose estimation by fusing color and depth 全局点到超平面ICP:通过融合颜色和深度进行局部和全局姿态估计
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170402
F. I. Muñoz, Andrew I. Comport
{"title":"Global Point-to-hyperplane ICP: Local and global pose estimation by fusing color and depth","authors":"F. I. Muñoz, Andrew I. Comport","doi":"10.1109/MFI.2017.8170402","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170402","url":null,"abstract":"RGB-D view registration has been widely studied by the robotics and computer vision community. The well known Iterative Closest Points (ICP) method and its variants prevail for estimating the relative pose between sensors. However, the optimization is performed locally and by consequence it can get trapped in local minima. Global registration methods have been introduced as an approach to solve the local minima problem by exploiting the geometric structure of SE(3), and accelerated with local approaches. In this paper, a local hybrid approach named Point-to-hyperplane ICP has been combined with a global Branch and Bound strategy in order to estimate the 6DOF (degrees of freedom) pose parameters. Registration is performed by considering color and geometry at both, the matching and the error minimization stages. Results in real and synthetic environments demonstrate that the proposed method can improve global registration under challenging conditions such as partial overlapping and noisy datasets.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124098093","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: 5
Distributed filtering based on randomized gossip strategy 基于随机八卦策略的分布式过滤
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170446
Liangyu Jiang, Chao Wan, Yongxin Gao, Z. Duan
{"title":"Distributed filtering based on randomized gossip strategy","authors":"Liangyu Jiang, Chao Wan, Yongxin Gao, Z. Duan","doi":"10.1109/MFI.2017.8170446","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170446","url":null,"abstract":"This paper formulates and studies the problem of distributed filtering based on randomized gossip strategy in order to estimate the state of a dynamic system via all sensors in a network. First we introduce the randomized gossip algorithm by which the fastest averaging strategy can be obtained for a network with an arbitrary topology. Then we combine the randomized gossip algorithm with the information filter to design a randomized gossip based distributed filtering algorithm. The proposed method can adopt different communication volume flexibly, which results in different estimation performance. This flexibility distinguishes our method from the existing ones. Simulation examples verify that our method outperforms the diffusion strategy based distributed filtering algorithm if a small increase of communication requirements is allowed.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124103170","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: 5
Kinematic design and system implementation of jumping robot legs 跳跃机器人腿的运动学设计与系统实现
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170386
Myeongjin Kim, D. Yun
{"title":"Kinematic design and system implementation of jumping robot legs","authors":"Myeongjin Kim, D. Yun","doi":"10.1109/MFI.2017.8170386","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170386","url":null,"abstract":"Currently, research on a jumping robot has been actively conducted in the Robotics field. In this paper, we propose a linkage structure of the jumping robot leg. The trajectory and take-off angle are simulated by using a free software, LINKAGE program. Also, we introduced the two types of the propulsion structure.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123438418","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
Probabilistic state estimation using force sensor information in contact motion of arm robot 基于力传感器信息的手臂机器人接触运动概率状态估计
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170449
H. Kubota, Yuichi Kobayashi
{"title":"Probabilistic state estimation using force sensor information in contact motion of arm robot","authors":"H. Kubota, Yuichi Kobayashi","doi":"10.1109/MFI.2017.8170449","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170449","url":null,"abstract":"This paper presents a method for estimating object poses based on force sensor information using a particle filter. Autonomous robots often face uncertainty of measurement of object position when they manipulate objects. Related studies dealing with uncertainty in manipulation mainly focused on motions while grasping objects. In contrast, however, a non-grasp contact motion can be advantageous because it can be achieved with simple mechanical structures. In this paper, we propose a method of estimating the object's pose in the object-contact motion without a rigid grasp. We apply a unilateral constraint to the state estimation by combining a contact force calculation model with the particle filter estimation scheme. The proposed method was evaluated using simulation, where force sensor information was effectively utilized to estimate the object's pose. Moreover, it was confirmed that the information of not sensing any force also helped the robot to narrow its state distribution when considering the interference between the robot and the object.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121592185","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
Fusion of color and range sensors for occupant recognition and tracking 融合颜色和距离传感器，用于乘员识别和跟踪
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170348
Tianna-Kaye Woodstock, A. Sanderson
{"title":"Fusion of color and range sensors for occupant recognition and tracking","authors":"Tianna-Kaye Woodstock, A. Sanderson","doi":"10.1109/MFI.2017.8170348","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170348","url":null,"abstract":"This paper addresses the design and implementation of multisensor systems techniques that provide occupant knowledge and enable effective use of the smart space. The integration of feature selection and Bayesian classification provides consistent detection of occupants and occupant locations. Additionally, occupancy detection and activity recognition are extended through the multisensor fusion of time-of-flight (ToF) and color sensors. The existing color sensors do not provide high-resolution detection, and ToF range information and light source geometry are needed to extend this information. In this multisensor approach, predictive filter tracking techniques in the color space of the sensor response are explored in order to provide more consistent and robust detection and monitoring.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125503192","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: 3
Investigating dynamic polling intervals for wireless sensor network applications with bursty traffic 研究具有突发流量的无线传感器网络应用的动态轮询间隔
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170361
S. Siddiqui, A. Khan, S. Ghani
{"title":"Investigating dynamic polling intervals for wireless sensor network applications with bursty traffic","authors":"S. Siddiqui, A. Khan, S. Ghani","doi":"10.1109/MFI.2017.8170361","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170361","url":null,"abstract":"A large number of WSN applications with bursty traffic have emerged in the recent past such as rare event detection and forest fire monitoring. Bursty traffic in Wireless Sensor Networks (WSNs) offers various challenges in terms of reliability and latency. Unlike periodic monitoring, energy consumption is far more challenging to deal with in WSN applications with bursty traffic due to long periods of inactivity followed by certain bursts. In this paper, we study the influence of using dynamic channel polling intervals for dealing with the bursty traffic. We refer to dynamic polling as the polling process based on probabilistic distribution chosen in correspondence to the incoming traffic. A simple asynchronous MAC protocol has been developed to illustrate the difference in WSN performance when deterministic, exponential and dynamic polling interval distributions are used. The experiments have been conducted using TinyOS source code and Avrora emulator to evaluate the proposed scheme in terms of energy consumption and latency. Performance improvement brought to WSN through dynamic polling has been validated by comparing the results with those of SCP-MAC. It has been found that the MAC with the dynamic polling intervals outperforms deterministic and exponential polling distributions as well as SCP-MAC for the bursty traffic.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128346830","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: 5
Operation action recognition using wearable devices with inertial sensors 使用带有惯性传感器的可穿戴设备进行操作动作识别
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170376
Yanyan Bao, F. Sun, Xinfeng Hua, Bin Wang, Jianqin Yin
{"title":"Operation action recognition using wearable devices with inertial sensors","authors":"Yanyan Bao, F. Sun, Xinfeng Hua, Bin Wang, Jianqin Yin","doi":"10.1109/MFI.2017.8170376","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170376","url":null,"abstract":"The recognition of the operations are important for the factories management. In the paper, we propose an operation action recognition method based on two wearable devices containing inertial sensors. A mobile solution that leverages wristbands to capture the motions of operations is comfortable for workers. We first design the features based on the physical parameters of operation actions. To systematically preprocess the data, the features which consist of spectral entropy, the sum of acceleration, angular rate and angle are extracted. Support Vector Machine is used to recognize the operations. Four different operation actions of different persons are implemented in the experiments. The results indicate that the proposed method can recognition operation actions with high accuracy.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114519686","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: 3
Online decision making for stream-based robotic sampling via submodular optimization 基于子模块优化的流机器人采样在线决策
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170416
Wenhao Luo, Changjoo Nam, K. Sycara
{"title":"Online decision making for stream-based robotic sampling via submodular optimization","authors":"Wenhao Luo, Changjoo Nam, K. Sycara","doi":"10.1109/MFI.2017.8170416","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170416","url":null,"abstract":"We consider the problem of online robotic sampling in environmental monitoring tasks where the goal is to collect k best samples from n sequentially occurring measurements. In contrast to many existing works that seek to maximize the utility of the selected samples online, we aim to find the cardinality constrained subset of streaming measurements under irrevocable sampling decisions so that the prediction over untested measurements is most accurate. Using the information theoretic criterion, we present an online submodular algorithm for stream-based sample selection with a provable performance bound. We demonstrate the effectiveness of our algorithm via simulations of information gathering from indoor static sensors.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114548928","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: 5
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