2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)最新文献

Artificial Neural Networks Aided Image Localization for Pedestrian Dead Reckoning for Indoor Navigation Applications 基于人工神经网络的室内导航行人航位推算图像定位

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-12-04 DOI: 10.1109/UPINLBS.2018.8559833

J. Liao, K. Chiang, Hsiu-Wen Chang, Yu-Hua Li

{"title":"Artificial Neural Networks Aided Image Localization for Pedestrian Dead Reckoning for Indoor Navigation Applications","authors":"J. Liao, K. Chiang, Hsiu-Wen Chang, Yu-Hua Li","doi":"10.1109/UPINLBS.2018.8559833","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559833","url":null,"abstract":"Recently, indoor navigation has become popular because of the popular smartphone and the growth of Location Based Services (LBS). Pedestrian Dead Reckoning (PDR) has the good potential to confront the challenges in environments lacking a Global Navigation Satellite System (GNSS). However, PDR has inherent errors that accumulated step by step. An image-based localization can as an aiding system, because virtually all mobile devices contain a basic camera sensor. However, the image-based localization requires successive and overlapped images for continuously positioning. In addition, the solutions provided by either image-based localization or a PDR are usually in a relative coordinate system. Therefore, this study proposes a system, which uses space resection-aided PDR with georeferenced images of a previously mapped environment. In order to implement the procedure automatically and reduce the image processing, this study further uses markers in the georeferenced images. After that, Artificial Neural Network (ANN) is novel applied to estimate the distance between the marker and camera. Since the marker is also georeferenced, the camera position is updated through the detected georeferenced marker, estimated distance, and orientation from inertial sensor, and then update the PDR result. The indoor mobile mapping system (IMMS) is used for the effective production of georeferenced images. The result shows the proposed system is able to initialize in indoor without manually given initial position and provides long-term accurate navigation.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114976904","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

Hierarchical calibration architecture based on Inertial/magnetic sensors for indoor positioning 基于惯性/磁传感器的室内定位分层标定体系

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-09-01 DOI: 10.1109/UPINLBS.2018.8559914

Gong-xu Liu, Ling-feng Shi, Jian‐Hui Xun, Sen Chen, Hong Liu, Yi-Fan Shi

{"title":"Hierarchical calibration architecture based on Inertial/magnetic sensors for indoor positioning","authors":"Gong-xu Liu, Ling-feng Shi, Jian‐Hui Xun, Sen Chen, Hong Liu, Yi-Fan Shi","doi":"10.1109/UPINLBS.2018.8559914","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559914","url":null,"abstract":"Indoor positioning system based on MEMS (Micro-electromechanical Systems) technology is really self-measurement technology which does not depend on any external signal and other infrastructure. Inertial/magnetic sensors based on MEMS technology are typically composed of three orthogonal gyroscopes, three orthogonal accelerometers and three orthogonal magnetometers, which can form AHRS (attitude heading reference system) and pedestrian dead reckoning system. However, the inertial/magnetic sensors can offer good short term positioning in indoor environments, but the absolute position fixes of medium- to long- term have always been the research hotspot and difficulty. Therefore, a hierarchical calibration architecture based on inertial/magnetic sensors is proposed creatively, including the data layer–a axial-based temperature variation model is proposed to calibrate the raw data from MEMS sensors by the analysis and modeling of sensor error characteristics; the signal layer–a triple zero velocity update method is proposed to update integral initial value according to the pedestrians’ gait characteristic; the information layer–an intelligent information fusion method is proposed based on machine learning to calibrate the orientation and positioning. Besides, a 3-D indoor positioning platform is set up based on MPU9250, and the effectiveness of the proposed hierarchical calibration architecture is verified based on the platform.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125623008","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

Improving Pedestrian Dead Reckoning using Likely and Unlikely Paths 使用可能路径和不可能路径改进行人航位推算

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559709

Johann-Peter Wolff, S. Stieber, Fabian Hölzke, Daniel Gis, C. Haubelt, P. Lepidis, J. Meier

引用次数: 1

CSI-based Indoor Tracking with Positioning-Assisted 基于csi的定位辅助室内跟踪

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559817

Hang Liu, Ning-xia Xia, Dawei Guo, Qing Pan

引用次数: 6

A Point-Line Feature based Visual SLAM Method in Dynamic Indoor Scene 基于点-线特征的动态室内场景视觉SLAM方法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559749

Runzhi Wang, Yongkang Wang, W. Wan, K. Di

{"title":"A Point-Line Feature based Visual SLAM Method in Dynamic Indoor Scene","authors":"Runzhi Wang, Yongkang Wang, W. Wan, K. Di","doi":"10.1109/UPINLBS.2018.8559749","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559749","url":null,"abstract":"Traditional visual SLAM methods employ point features to implement motion estimation and environment map construction. However, in some low-texture indoor scenarios, such as office and corridor, less reliable point features may be found, which could jeopardize the SLAM solution. In addition, scene changes among sequential images are not only caused by the position changes of image acquisition, but also by pedestrians and other moving objects in an indoor dynamic environment. Thus, feature identification for moving objects is needed as an important part for practical application of indoor SLAM. This paper proposes a point-line feature based SLAM method that combines both of points and line segments to enhance the performance of feature extraction in indoor scene, which can extract many line features from walls, furniture and other artificial objects. In this method, added line features help to gain more robust and accurate results. Additionally, a real-time object detection algorithm is introduced to identify the features extracted from pedestrians, so that to eliminate the negative effects caused by moving objects. The experimental results demonstrates that the proposed method can obtain more robust and accurate localization results in dynamic indoor scene.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133298752","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

A High-precision Dynamic Indoor Localization Algorithm Based on UWB Technology 基于超宽带技术的高精度动态室内定位算法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559902

B. Wang, Yanyan Liu, Qingquan Li, Zhuang Yan

引用次数: 8

Acoustic Backscatter: Enabling Ultra-Low Power, Precise Indoor Positioning. 声学后向散射:实现超低功耗、精确室内定位。

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559762

Bert Cox, L. D. Strycker, L. Perre

引用次数: 6

Wireless clock synchronization based on UWB positioning system and its ranging optimization 基于超宽带定位系统的无线时钟同步及其测距优化

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559938

Lili Ge, Z. Deng, Wen Liu, Guolong Zhang, Ke Han, Jichao Jiao

引用次数: 3

Positioning in Large Indoor Spaces using Smartphone Camera based on Static Objects 基于静态物体的智能手机相机在大型室内空间中的定位

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559754

Aoran Xiao, Ruizhi Chen, Dewen Wu, Yujin Chen

引用次数: 1

Fusion algorithm of improved fingerprinting/PDR/Map based on Extended Kalman Filter (EKF)/Particle Filter(PF) 基于扩展卡尔曼滤波(EKF)/粒子滤波(PF)的改进指纹/PDR/地图融合算法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559712

Wen Liu, Jing Li, Z. Deng, Xiao Fu

{"title":"Fusion algorithm of improved fingerprinting/PDR/Map based on Extended Kalman Filter (EKF)/Particle Filter(PF)","authors":"Wen Liu, Jing Li, Z. Deng, Xiao Fu","doi":"10.1109/UPINLBS.2018.8559712","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559712","url":null,"abstract":"The indoor location service has become the hotspot in both academia and industry due to the increasing demands of navigation for complex indoor environments. In order to improve the performance of positioning systems in indoor dense cluttered environments, an improved fingerprinting algorithm which fuses Received Signal Strength Indication (RSSI) and Time of Arrival (TOA) (named FP-RT) is proposed to mitigate the fluctuation of RSSI measurements caused by indoor multi-path fading and building obstacles. Meanwhile, a novel indoor positioning system integrating FP-RT, Pedestrian Dead Reckon (PDR) and map information based on second-order cascaded Extended Kalman Filter (EKF) and Particle Filter (PF) (FPM-EP) is proposed to decrease fluctuation of fingerprinting and accumulated error pf PDR. In FPM-EP system, the estimated results of FP-RT are used to decrease the cumulative drifts of Pedestrian Dead Reckon (PDR) based on Extended Kalman Filter (EKF). Moreover, the map information is introduced to constrain the FP-RT/PDR-derived position via Particle Filter (PF). Experiments are conducted in indoors, and the performance of the FP-RT, the fingerprint-based positioning without TOA-aided (named FPWT), the FPM-EP algorithm, EKF-based positioning and PF-based positioning were evaluated. The experimental results show that the mean error of FP-RT is 1.67m, which is improved by 22% in terms of positioning accuracy. Moreover, the mean accuracy of the proposed fusion system is 0.84m, which is an improvement by 49.7% and 64.2%, 34.8% and 33.3%, compared with FP-RT, PDR, EKF-based and PF-based algorithm respectively.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128770498","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