2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第8页

A method for post-mission velocity and orientation estimation based on data fusion from MEMS-IMU and GNSS 基于MEMS-IMU和GNSS数据融合的任务后速度和方向估计方法

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170383

P. Davidson, R. Piché

引用次数: 4

A multi-agent coverage algorithm with connectivity maintenance 具有连通性维护的多代理覆盖算法

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170372

Sungjoon Choi, Kyungjae Lee, Songhwai Oh

引用次数: 1

Entropy-based abnormal activity detection fusing RGB-D and domotic sensors 融合RGB-D和国内传感器的基于熵的异常活动检测

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170405

M. Fernández-Carmona, S. Coşar, Claudio Coppola, N. Bellotto

{"title":"Entropy-based abnormal activity detection fusing RGB-D and domotic sensors","authors":"M. Fernández-Carmona, S. Coşar, Claudio Coppola, N. Bellotto","doi":"10.1109/MFI.2017.8170405","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170405","url":null,"abstract":"The automatic detection of anomalies in Active and Assisted Living (AAL) environments is important for monitoring the wellbeing and safety of the elderly at home. The integration of smart domotic sensors (e.g. presence detectors) and those ones equipping modern mobile robots (e.g. RGB-D cameras) provides new opportunities for addressing this challenge. In this paper, we propose a novel solution to combine local activity levels detected by a single RGB-D camera with the global activity perceived by a network of domotic sensors. Our approach relies on a new method for computing such a global activity using various presence detectors, based on the concept of entropy from information theory. This entropy effectively shows how active a particular room or environment's area is. The solution includes also a new application of Hybrid Markov Logic Networks (HMLNs) to merge different information sources for local and global anomaly detection. The system has been tested with a comprehensive dataset of RGB-D and domotic data containing data entries from 37 different domotic sensors (presence, temperature, light, energy consumption, door contact), which is made publicly available. The experimental results show the effectiveness of our approach and its potential for complex anomaly detection in AAL settings.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"7 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":"127527697","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

Target motion analysis with evolutionary search by fusion of two moving acoustic sensors 基于两个运动声传感器融合的进化搜索目标运动分析

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170436

Hyunhak Shin, W. Hong, Ria Kim, Hanseok Ko

引用次数: 0

Real-time human collision detection for industrial robot cells 面向工业机器人单元的实时人体碰撞检测

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170368

Erind Ujkani, Petter S. Eppeland, Atle Aalerud, G. Hovland

引用次数: 3

On weak points of the ellipsoidal intersection fusion 关于椭球交点融合的弱点

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170403

Jiří Ajgl, O. Straka

引用次数: 3

Real-time 3D scene modeling using dynamic billboard for remote robot control systems 利用动态广告牌进行远程机器人控制系统的实时三维场景建模

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170454

P. Chu, Seoungjae Cho, Hieu Trong Nguyen, Sungdae Sim, K. Kwak, Kyungeun Cho

{"title":"Real-time 3D scene modeling using dynamic billboard for remote robot control systems","authors":"P. Chu, Seoungjae Cho, Hieu Trong Nguyen, Sungdae Sim, K. Kwak, Kyungeun Cho","doi":"10.1109/MFI.2017.8170454","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170454","url":null,"abstract":"In this paper, a method for modeling three-dimensional scenes from a Lidar point cloud as well as a billboard calibration approach for remote mobile robot control applications are presented as a combined two-step approach. First, by projecting a local three-dimensional point cloud on two-dimensional coordinate system, we obtain a list of colored points. Based on this list, we apply a proposed ground segmentation algorithm to separate ground and non-ground areas. With the ground part, a dynamic triangular mesh is created by means of a height map and the vehicle position. The non-ground part is divided into small groups. Then, a local voxel map is applied for modeling each group. As a result, all the inner surfaces are eliminated. Second, for billboard calibration, we implement three stages in each frame. In the first stage, at the billboard location, an average ground point is estimated. In the second stage, the distortion angle is calculated. The billboard is updated for each frame in the final stage and corresponds to the terrain gradient.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"10 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":"128651732","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

Dynamic Covariance Estimation — A parameter free approach to robust Sensor Fusion 动态协方差估计——一种无参数鲁棒传感器融合方法

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170347

Tim Pfeifer, Sven Lange, P. Protzel

{"title":"Dynamic Covariance Estimation — A parameter free approach to robust Sensor Fusion","authors":"Tim Pfeifer, Sven Lange, P. Protzel","doi":"10.1109/MFI.2017.8170347","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170347","url":null,"abstract":"In robotics, non-linear least squares estimation is a common technique for simultaneous localization and mapping. One of the remaining challenges are measurement outliers leading to inconsistency or even divergence within the optimization process. Recently, several approaches for robust state estimation dealing with outliers inside the optimization back-end were presented, but all of them include at least one arbitrary tuning parameter that has to be set manually for each new application. Under changing environmental conditions, this can lead to poor convergence properties and erroneous estimates. To overcome this insufficiency, we propose a novel robust algorithm based on a parameter free probabilistic foundation called Dynamic Covariance Estimation. We derive our algorithm directly from the probabilistic formulation of a Gaussian maximum likelihood estimator. Through including its covariance in the optimization problem, we empower the optimizer to approximate these to the sensor's real properties. Finally, we prove the robustness of our approach on a real world wireless localization application where two similar state-of-the-art algorithms fail without extensive parameter tuning.","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":"128212616","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: 18

Implementation of semantic segmentation for road and lane detection on an autonomous ground vehicle with LIDAR 基于激光雷达的自动地面车辆道路和车道检测语义分割的实现

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170358

Kai Li Lim, T. Drage, T. Bräunl

{"title":"Implementation of semantic segmentation for road and lane detection on an autonomous ground vehicle with LIDAR","authors":"Kai Li Lim, T. Drage, T. Bräunl","doi":"10.1109/MFI.2017.8170358","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170358","url":null,"abstract":"While current implementations of LIDAR-based autonomous driving systems are capable of road following and obstacle avoidance, they are still unable to detect road lane markings, which is required for lane keeping during autonomous driving sequences. In this paper, we present an implementation of semantic image segmentation to enhance a LIDAR-based autonomous ground vehicle for road and lane marking detection, in addition to object perception and classification. To achieve this, we installed and calibrated a low-cost monocular camera onto a LIDAR-fitted Formula-SAE Electric car as our test bench. Tests were performed first on video recordings of local roads to verify the feasibility of semantic segmentation, and then on the Formula-SAE car with LIDAR readings. Results from semantic segmentation confirmed that the road areas in each video frame were properly segmented, and that road edges and lane markers can be classified. By combining this information with LIDAR measurements for road edges and obstacles, distance measurements for each segmented object can be obtained, thereby allowing the vehicle to be programmed to drive autonomously within the road lanes and away from road edges.","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":"130100508","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: 11

Fast and robust localization using laser rangefinder and wifi data 快速和强大的定位使用激光测距仪和wifi数据

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170415

Renato Miyagusuku, Yiploon Seow, A. Yamashita, H. Asama

{"title":"Fast and robust localization using laser rangefinder and wifi data","authors":"Renato Miyagusuku, Yiploon Seow, A. Yamashita, H. Asama","doi":"10.1109/MFI.2017.8170415","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170415","url":null,"abstract":"Laser rangefinders are very popular sensors in robot localization due to their accuracy. Typically, localization algorithms based on these sensors compare range measurements with previously obtained maps of the environment. As many indoor environments are highly symmetrical (e.g., most rooms have the same layout and most corridors are very similar) these systems may fail to recognize one location from another, leading to slow convergence and even severe localization problems. To address these two issues we propose a novel system which incorporates WiFi-based localization into a typical Monte Carlo localization algorithm that primarily uses laser rangefinders. Our system is mainly composed of two modules other than the Monte Carlo localization algorithm. The first uses WiFi data in conjunction with the occupancy grid map of the environment to solve convergence of global localization fast and reliably. The second detects possible localization failures using a metric based on WiFi models. To test the feasibility of our system, we performed experiments in an office environment. Results show that our system allows fast convergence and can detect localization failures with minimum additional computation. We have also made all our datasets and software readily available online for the community.","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":"116990639","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: 4