2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第7页

Localization based on multiple visual-metric maps 基于多个视觉度量地图的定位

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170431

Adi Sujiwo, E. Takeuchi, Luis Yoichi Morales Saiki, Naoki Akai, Y. Ninomiya, M. Edahiro

{"title":"Localization based on multiple visual-metric maps","authors":"Adi Sujiwo, E. Takeuchi, Luis Yoichi Morales Saiki, Naoki Akai, Y. Ninomiya, M. Edahiro","doi":"10.1109/MFI.2017.8170431","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170431","url":null,"abstract":"This paper presents a fusion of monocular camera-based metric localization, IMU and odometry in dynamic environments of public roads. We build multiple vision-based maps and use them at the same time in localization phase. For the mapping phase, visual maps are built by employing ORB-SLAM and accurate metric positioning from LiDAR-based NDT scan matching. This external positioning is utilized to correct for scale drift inherent in all vision-based SLAM methods. Next in the localization phase, these embedded positions are used to estimate the vehicle pose in metric global coordinates using solely monocular camera. Furthermore, to increase system robustness we also proposed utilization of multiple maps and sensor fusion with odometry and IMU using particle filter method. Experimental testing were performed through public road environment as far as 170 km at different times of day to evaluate and compare localization results of vision-only, GNSS and sensor fusion methods. The results show that sensor fusion method offers lower average errors than GNSS and better coverage than vision-only one.","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":"115498409","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: 8

3D reconstruction of line features using multi-view acoustic images in underwater environment 水下环境下多视点声学图像的线特征三维重建

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170447

Ngoc Trung Mai, Hanwool Woo, Yonghoon Ji, Y. Tamura, A. Yamashita, H. Asama

{"title":"3D reconstruction of line features using multi-view acoustic images in underwater environment","authors":"Ngoc Trung Mai, Hanwool Woo, Yonghoon Ji, Y. Tamura, A. Yamashita, H. Asama","doi":"10.1109/MFI.2017.8170447","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170447","url":null,"abstract":"In order to understand the underwater environment, it is essential to use sensing methodologies able to perceive the three dimensional (3D) information of the explored site. Sonar sensors are commonly employed in underwater exploration. This paper presents a novel methodology able to retrieve 3D information of underwater objects. The proposed solution employs an acoustic camera, which represents the next generation of sonar sensors, to extract and track the line of the underwater objects which are used as visual features for the image processing algorithm. In this work, we concentrate on artificial underwater environments, such as dams and bridges. In these structured environments, the line segments are preferred over the points feature, as they can represent structure information more effectively. We also developed a method for automatic extraction and correspondences matching of line features. Our approach enables 3D measurement of underwater objects using arbitrary viewpoints based on an extended Kalman filter (EKF). The probabilistic method allows computing the 3D reconstruction of underwater objects even in presence of uncertainty in the control input of the camera's movements. Experiments have been performed in real environments. Results showed the effectiveness and accuracy of the proposed solution.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"79 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":"125055790","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

Development of robot manipulation technology in ROS environment ROS环境下机器人操作技术的发展

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170364

Dong-Eon Kim, Dongju Park, Jeong-Hwan Moon, Ki-Seo Kim, Jin‐Hyun Park, Jangmyung Lee

引用次数: 1

Wearable gesture control of agile micro quadrotors 敏捷微型四旋翼机的可穿戴手势控制

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170439

Yunho Choi, Inhwan Hwang, Songhwai Oh

{"title":"Wearable gesture control of agile micro quadrotors","authors":"Yunho Choi, Inhwan Hwang, Songhwai Oh","doi":"10.1109/MFI.2017.8170439","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170439","url":null,"abstract":"Quadrotor unmanned aerial vehicles (UAVs) have seen a surge of use in various applications due to its structural simplicity and high maneuverability. However, conventional control methods using joysticks prohibit novices from getting used to maneuvering quadrotors in short time. In this paper, we suggest the use of a wearable device, such as a smart watch, as a new remote-controller for a quadrotor. The user's command is recognized as gestures using the 9-DoF inertial measurement unit (IMU) of a wearable device through a recurrent neural network (RNN) with long short-term memory (LSTM) cells. Our implementation also makes it possible to align the heading of a quadrotor with the heading of the user. Our implementation allows nine different gestures and the trained RNN is used for real-time gesture recognition for controlling a micro quadrotor. The proposed system exploits available sensors in a wearable device and a quadrotor as much as possible to make the gesture-based control intuitive. We have experimentally validated the performance of the proposed system by using a Samsung Gear S smart watch and a Crazyflie Nano Quadcopter.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"42 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":"128849876","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

Detection and classification of stochastic features using a multi-Bayesian approach 基于多贝叶斯方法的随机特征检测与分类

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170421

J. J. Steckenrider, T. Furukawa

{"title":"Detection and classification of stochastic features using a multi-Bayesian approach","authors":"J. J. Steckenrider, T. Furukawa","doi":"10.1109/MFI.2017.8170421","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170421","url":null,"abstract":"This paper introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. This approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level classification. Unlike conventional methods, these features' uncertainties are characterized so that test data can be correctively cast into the feature space with probability distribution functions that can be integrated over class decision boundaries created by a quadratic Bayesian classifier. The proposed approach is specifically formulated for road crack detection and characterization, which is one of the potential applications. For test images assessed with this technique, ground truth was estimated accurately and consistently with effective Bayesian correction, showing a 25% improvement in recall rate over standard classification. Application to road cracks demonstrated successful detection and classification in a practical domain. The proposed approach is extremely effective in characterizing highly probabilistic features in noisy environments when several correlated observations are available either from multiple sensors or from data sequentially obtained by a single sensor.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"65 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":"130625631","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

UJI RobInLab's approach to the Amazon Robotics Challenge 2017 UJI RobInLab参加2017年亚马逊机器人挑战赛的方法

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170448

A. P. Pobil, Majd Kassawat, A. J. Duran, M. Arias, N. Nechyporenko, Arijit Mallick, E. Cervera, Dipendra Subedi, Ilia Vasilev, D. Cardin, Emanuele Sansebastiano, Ester Martínez-Martín, A. Morales, Gustavo A. Casañ, A. Arenal, B. Goriatcheff, C. Rubert, G. Recatalá

引用次数: 9

Design of multiple classifier systems based on testing sample pairs 基于测试样本对的多分类器系统设计

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170429

Gaochao Feng, Deqiang Han, Yi Yang, Jiankun Ding

引用次数: 0

On state estimation and fusion with elliptical constraints 椭圆约束下的状态估计与融合

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170411

Qiang Liu, N. Rao

引用次数: 3

3D handheld scanning based on multiview 3D registration using Kinect Sensing device 基于Kinect传感设备的多视角三维配准的手持式3D扫描

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170450

Shirazi Muhammad Ayaz, Danish Khan, M. Y. Kim

引用次数: 1

A nearest neighbour ensemble Kalman Filter for multi-object tracking 多目标跟踪的最近邻集成卡尔曼滤波

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170433

Fabian Sigges, M. Baum

引用次数: 4