2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第6页

Map-based drone homing using shortcuts 基于地图的无人机导航使用快捷键

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170371

D. Bender, W. Koch, D. Cremers

{"title":"Map-based drone homing using shortcuts","authors":"D. Bender, W. Koch, D. Cremers","doi":"10.1109/MFI.2017.8170371","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170371","url":null,"abstract":"Up to the present day, GPS signals are the key component in almost all outdoor navigation tasks of robotic platforms. To obtain the platform pose, comprising the position as well as the orientation, and receive information at a higher frequency, the GPS signals are commonly used in a GPS-corrected inertial navigation system (INS). The GPS is a critical single point of failure, especially for autonomous drones. We propose an approach which creates a metric map of the observed area by fusing camera images with inertial and GPS data during its normal operation and use this map to steer a drone efficiently to its home position in the case of an GPS outage. A naive approach would follow the previously traveled path and get accurate pose estimates by comparing the current camera image with the previously created map. The presented procedure allows the usage of shortcuts through unexplored areas to minimize the travel distance. Thereby, we ensure to reach the starting point by taking into consideration the maximal positional drift while performing pure visual navigation in unknown areas. We achieved close to optimal results in intensive numerical studies and we demonstrate the usability of the algorithm in a realistic simulation environment.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"11 2 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":"116069103","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: 13

Design & implementation of distributed congestion control scheme for heterogeneous traffic in wireless sensor networks 无线传感器网络中异构流量分布式拥塞控制方案的设计与实现

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170384

A. Khan, S. Ghani, S. Siddiqui

引用次数: 2

Image brightness adjustment system based on ANFIS by RGB and CIE L∗a∗b∗ 基于RGB和CIE L * a * b *的ANFIS图像亮度调节系统

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170365

Eunkyeong Kim, Hyunhak Cho, Hansoo Lee, Jongeun Park, Sungshin Kim

{"title":"Image brightness adjustment system based on ANFIS by RGB and CIE L∗a∗b∗","authors":"Eunkyeong Kim, Hyunhak Cho, Hansoo Lee, Jongeun Park, Sungshin Kim","doi":"10.1109/MFI.2017.8170365","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170365","url":null,"abstract":"This paper proposes the method to adjust brightness information by applying CIE L∗a∗b∗ color space and adaptive neuro-fuzzy inference system. The image which is already captured by vision sensor should be adjusted brightness to recognize objects in an image. In case of proper intensity of lights, the clarity of an image is good to recognize objects. However, in case of improper intensity of lights, the image has darkish regions. It will leads to reduce success of object recognition. To make up for this week point, we adjust the image, which is a darkish image, by controlling brightness information of an image. Brightness information can be represented by CIE L∗a∗b∗ color space. So based on CIE L∗a∗b∗ color space, adaptive neuro-fuzzy inference system is implemented as control function. Control function carries out adjusting of brightness information by dealing with the value of L component of CIE L∗a∗b∗ color space. L component describes brightness information of an image. The values which is calculated by adaptive neuro-fuzzy inference system is called the adjustment coefficient. Finally, the adjustment coefficient is added to L component for adjusting brightness information. To verify the propose method, we calculated color difference with respect to RGB and CIE L∗a∗b∗ color space. As experimental results, the propose method can reduce color difference and makes the target image will be similar with reference image under proper intensity of lights.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"13 2 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":"117265221","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

Object map building on various terrains for a Wheeled mobile robot 为轮式移动机器人在各种地形上建立物体地图

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170350

J. Oh, Beomhee Lee

引用次数: 0

Detection-level fusion for multi-object perception in dense traffic environment 密集交通环境下多目标感知的检测级融合

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170355

Bin Huang, Hui Xiong, Jianqiang Wang, Qing Xu, Xiaofei Li, Keqiang Li

引用次数: 1

First approach of an optical localization and tracking method applied to a micro-conveying system 一种应用于微输送系统的光学定位与跟踪方法

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170424

Saly Malak, Hani Al Hajjar, E. Dupont, F. Lamarque

引用次数: 3

Development of an upper limb exoskeleton for rehabilitation training in virtual environment 虚拟环境中用于康复训练的上肢外骨骼研制

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170425

Qingcong Wu, Xingsong Wang

引用次数: 2

LIDAR-data accumulation strategy to generate high definition maps for autonomous vehicles 为自动驾驶汽车生成高清地图的激光雷达数据积累策略

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170357

Mohammad Aldibaja, Noaki Suganuma, Keisuke Yoneda

引用次数: 21

Side channel attack on digital door lock with vibration signal analysis: Longer password does not guarantee higher security level 带振动信号分析的数字门锁侧信道攻击:密码越长，安全性越高

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170414

Young-Mok Ha, Soohee Jang, Kwang-Won Kim, J. Yoon

引用次数: 4

A deep neural network approach to fusing vision and heteroscedastic motion estimates for low-SWaP robotic applications 一种融合视觉和异方差运动估计的深度神经网络方法，用于低swap机器人应用

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170407

Jared Shamwell, W. Nothwang, D. Perlis

{"title":"A deep neural network approach to fusing vision and heteroscedastic motion estimates for low-SWaP robotic applications","authors":"Jared Shamwell, W. Nothwang, D. Perlis","doi":"10.1109/MFI.2017.8170407","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170407","url":null,"abstract":"Due both to the speed and quality of their sensors and restrictive on-board computational capabilities, current state-of-the-art (SOA) size, weight, and power (SWaP) constrained autonomous robotic systems are limited in their abilities to sample, fuse, and analyze sensory data for state estimation. Aimed at improving SWaP-constrained robotic state estimation, we present Multi-Hypothesis DeepEfference (MHDE) — an unsupervised, deep convolutional-deconvolutional sensor fusion network that learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. This new multi-hypothesis formulation of our previous architecture, DeepEfference [1], has been augmented to handle dynamic heteroscedastic sensor and motion noise and computes hypothesis image mappings and predictions at 150–400 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel architectural pathways and n (1, 2, 4, or 8 in this work) multi-hypothesis generation subpathways to generate n pixel-level predictions and correspondences between source and target images. We evaluated MHDE on the KITTI Odometry dataset [2] and benchmarked it against DeepEfference [1] and DeepMatching [3] by mean pixel error and runtime. MHDE with 8 hypotheses outperformed DeepEfference in root mean squared (RMSE) pixel error by 103% in the maximum heteroscedastic noise condition and by 18% in the noise-free condition. MHDE with 8 hypotheses was over 5, 000% faster than DeepMatching with only a 3% increase in RMSE.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"1 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":"129357830","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