실내 위치기반서비스 기술과 논리지도 생성 방안

김종덕 ( Kim Jong-deok ) 텔코경영연구원 2017 텔코 저널 Vol.5 No.-

실내 위치기반서비스에 대한 수요가 빠르게 증가하고 있다. 실내 위치기반서비스 제공을 위해서는 디지털 실내 지도와 측위라는 두 가지 기본 기술 요소가 필요하다. 최근 스마트 폰을 이용하여 여러 사용자로부터 센싱 정보를 수집하고 이를 실내 지도 생성과 측위 등에 활용하는 접근이 주목 받고 있다. 본 기고에서는 이러한 접근을 포함 실내 위치기반서비스 관련 기술 개발 현황을 정리한다. 추가로 저자의 연구실에서 개발한 새로운 실내 논리지도 생성 방안을 소개한다. 이 방법은 기존 방법과 달리 비지도 대중참여(Unsupervised Crowdsourcing) 방식으로 참여자들의 행동을 제약하지 않고 일상 생활을 하여도 실내 지도 생성 지도에 기여하는 것을 특징으로 하고 있다. Demand for indoor location-based services is rapidly increasing. Two key fundamental components for indoor location-based services are digital indoor map and indoor localization technology. Recently, a kind of smart-phone based crowdsourcing approaches are attracting attentions in this field. In this approach, they make use of various sensor information gathered from users’ smart phones in the generation of indoor map and indoor localzation. This article summarizes the state-of-art technologies for indoor location based services including the afore mentioned crowdsourcing methods. In addition, it introduces a new crowdsourcing method developed by the research lab guided by the author that can generate an indoor logical map and can support indoor localization as well. Unsupervised crowdsourcing is the key characteristic of the proposed method. That is, contrary to the existing methods, it does not require supervised movement and action from the user for map creation and localization.

A Study of Multi-Target Localization Based on Deep Neural Network for Wi-Fi Indoor Positioning

유재현 사단법인 항법시스템학회 2021 Journal of Positioning, Navigation, and Timing Vol.10 No.1

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user’s position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed ultilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

Measurement-based AP Deployment Mechanism for Fingerprint-based Indoor Location Systems

( Dong Li ),( Yan Yan ),( Baoxian Zhang ),( Cheng Li ),( Peng Xu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.4

Recently, deploying WiFi access points (APs) for facilitating indoor localization has attracted increasing attention. However, most existing mechanisms in this aspect are typically simulation based and further they did not consider how to jointly utilize pre-existing APs in target environment and newly deployed APs for achieving high localization performance. In this paper, we propose a measurement-based AP deployment mechanism (MAPD) for placing APs in target indoor environment for assisting fingerprint based indoor localization. In the mechanism design, MAPD takes full consideration of pre-existing APs to assist the selection of good candidate positions for deploying new APs. For this purpose, we first choose a number of candidate positions with low location accuracy on a radio map calibrated using the pre-existing APs and then use over-deployment and on-site measurement to determine the actual positions for AP deployment. MAPD uses minimal mean location error and progressive greedy search for actual AP position selection. Experimental results demonstrate that MAPD can largely reduce the localization error as compared with existing work.

Development of Indoor Localization System using a Mobile Data Acquisition Platform and BoW Image Matching

이누리,김창재,최원석,편무욱,김용일 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.1

Recently, an increasing interest in the Location-Based Services (LBS) has led to the needs for studies on indoor localization. Therefore, this study aims to propose an image-based indoor localization system, which can improve the practicality and efficiency for wider applications. In order to construct reference imagery database, an optical camera and a 2D laser scanner mounted on a mobile platform were utilized and the data acquired was registered to the real world coordinate system. The localization procedure was then conducted based on Bag of Words (BoW) algorithm which matches the images by evaluating the similarities of the histograms produced from them. In addition, the experiments to verify the optimal system design were carried out. Compared to manual construction, the mobile platform-based data acquisition and location registration can be carried out in an easier manner. The proposed localization method provided more than 80% of localization accuracy for the query images taken in various locations and points of view. The system proposed herein is a practical, efficient, and accurate means of constructing the reference images and determining the location of a user in the indoor environment. It was also found that the size of codebook and the number of reference images should be determined while considering the computational time and the purpose of the application. Reducing the difference of the spatial resolution between the reference and the query images also provided more accurate results.

ARVisualizer : A Markerless Augmented Reality Approach for Indoor Building Information Visualization System

김희관,조현달 대한공간정보학회 2008 Spatial Information Research Vol.16 No.4

Augmented reality (AR) has tremendous potential in visualizing geospatial information, especially on the actual physical scenes. However, to utilize augmented reality in mobile system, many researches have undergone with GPS or ubiquitous marker based approaches. Although there are several papers written with vision based markerless tracking, previous approaches provide fairly good results only in largely under “controlled environments.” Localization and tracking of current position become more complex problem when it is used in indoor environments. Many proposed Radio Frequency (RF) based tracking and localization. However, it does cause deployment problems of large RF-based sensors and readers. In this paper, we present a noble markerless AR approach for indoor (possible outdoor, too) navigation system only using monoSLAM (Monocular Simultaneous Localization and Map building) algorithm to full-fill our grand effort to develop mobile seamless indoor/outdoor u-GIS system. The paper briefly explains the basic SLAM algorithm, then the implementation of our system.

ZigBee 실내 위치 인식 알고리즘의 정확도 평가

노안젤라송이 ( Angela Song Ie Noh ),이웅재 ( Woong Jae Lee ) 한국인터넷정보학회 2010 인터넷정보학회논문지 Vol.11 No.1

본 논문은 실내 위치 인식을 위하여 ZigBee 이동 장치의 위치를 측정하였으며 Bayesian Markov 위치 추론 기법을 적용하였다. 정확도 분석을 위해 기존의 지도 기반의 위치 인식 기법과 비교하였는데 이 기법은 이미 지정된 위치에서의 RSSI 데이터를 데이터베이스화하여 참조하도록 하는 반면 Bayesian Markov 추론 방법은 시간, 방향, 거리의 변화에 영향을 받았다. 이 두 가지 방법에 따른 측정은 지그비 모듈을 사용하여 RSSI를 측정한 결과를 토대로 이루어졌으며 그 결과 실내에서의 RSSI와 거리와의 관계로 접근하는 것이 바람직하며 Bayesian Markov에 의한 분석결과 기존의 지도 기반 위치 인식 기법에 비하여 높은 정확도를 보여주었다. 결과적으로 기존의 지도 기반 위치 인식 기법은 사전에 환경 요인에 대한 설정을 해주어야 하고, 보다 낮은 정확도를 가지고 있으므로 환경 변화가 잦은 실내에서는 부적합하다고 생각된다. This paper applies Bayesian Markov inferred localization techniques for determining ZigBee mobile device`s position. To evaluate its accuracy, we compare it with conventional technique, map-based localization. While the map-based localization technique referring to database of predefined locations and their RSSI data, the Bayesian Markov inferred localization is influenced by changes of time, direction and distance. All determinations are drawn from the estimation of Received Signal Strength (RSS) using ZigBee modules. Our results show the relationship between RSSI and distance in indoor ZigBee environment and higher localization accuracy of Bayesian Markov localization technique. We conclude that map-based localization is not suitable for flexible changes in indoors because of its predefined condition setup and lower accuracy comparing to distance-based Markov Chain inference localization system.

Fingerprint- and Kalman Filter-based Localization Exploiting Reference Signal Received Power Calibration

Chahyeon Eom,Sunghoon Jung,Chaehun Im,Chungyong Lee 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.3

This paper proposes a localization scheme exploiting reference signal received power (RSRP) for estimation of the next location. The proposed scheme can correct outliers without discarding data by adding RSRP as a state vector for a Kalman filter, and combining the Kalman filter with fingerprint-based localization. Performance evaluation is carried out via simulations in indoor environments. Results indicate that the proposed scheme can effectively correct outliers and enhance positioning accuracy. The root mean square error in the positioning error was reduced by 56%, compared to the conventional fingerprint-based localization schemes for indoor environments.

Cross-Technology Localization: Leveraging Commodity WiFi to Localize Non-WiFi Device

( Dian Zhang ),( Rujun Zhang ),( Haizhou Guo ),( Peng Xiang ),( Xiaonan Guo ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.11

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

Stereo AoA System for Indoor SLAM

Hee-Joong Kim,Jihong Lee 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10

In this paper, we address two systems composed of ODVS(Omni-directional Vision Sensor) for localizing mobile robots in indoor environments. One, with single AoA(Angle of Arrival) system, needs at least three landmarks of predefined location while the other, called stereo AoA system, requires only one landmark of known location for localization. For identifying the landmarks, image processing techniques are applied to omni-directional image to get colored information that is main features of indoor environments. The final goal of this research is to build up local map autonomously by image processing in indoor environments using lines on the image sequences. Through several experiments with the system, we confirmed the feasibility of the system for map building.

Automatic Building and Floor Classification using Two Consecutive Multi-layer Perceptron

Jaehoon Cha,Sanghyuk Lee,Kyeong Soo Kim 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10

Key issues of indoor localization is taking full advantages and overcoming its disadvantages. Indoor localization based on Wi-Fi fingerprinting attracts researchers’ attentions since it does not require new infrastructure and devices. Many devices such as smart phones and laptops, which have a function to capture Wi-Fi signals, can be used for Wi-Fi fingerprinting. However, due to unreliable Wi-Fi signals, there are still difficulty to achieve high positioning accuracy. The unreliable signal disturbs devices to find their locations. As a result, getting localization with devices sometimes makes a wrong decision in building classification. It is useless for people to find a destination floor if they are in different building. In this paper, we propose two consecutive multi-layer perceptrons to get more precise localization. With sumple structure, we get better performance and show precise decision results in building classification, which is critical in Wi-Fi fingerprinting. We use UJIndoorLoc dataset which is open dataset.