scholarly journals Can indoor navigation service incorporating signs support spatial learning?

2021 ◽  
Vol 3 ◽  
pp. 1-2
Author(s):  
Wangshu Wang ◽  
Haosheng Huang ◽  
Georg Gartner
Keyword(s):  
Spatial Learning ◽  
Indoor Navigation ◽  
Navigation Service

scholarly journals Towards an Automatic Ice Navigation Support System in the Arctic Sea

2021 ◽  
Author(s):  
Xintao Liu ◽  
Shahram Sattar ◽  
Songnian Li
Keyword(s):  
Support System ◽  
Indoor Navigation ◽  
The Arctic ◽  
Emergency Situations ◽  
Arctic Sea ◽  
Navigation Support ◽  
Northern Regions ◽  
Ice Navigation ◽  
Navigation Service ◽  
Sea Area

Conventional ice navigation in the sea is manually operated by well-trained navigators, whose experiences are heavily relied upon to guarantee the ship’s safety. Despite the increasingly available ice data and information, little has been done to develop an automatic ice navigation support system to better guide ships in the sea. In this study, using the vector-formatted ice data and navigation codes in northern regions, we calculate ice numeral and divide sea area into two parts: continuous navigable area and the counterpart numerous separate unnavigable area. We generate Voronoi Diagrams for the obstacle areas and build a road network-like graph for connections in the sea. Based on such a network, we design and develop a geographic information system (GIS) package to automatically compute the safest-and-shortest routes for different types of ships between origin and destination (OD) pairs. A visibility tool, Isovist, is also implemented to help automatically identify safe navigable areas in emergency situations. The developed GIS package is shared online as an open source project called NavSpace, available for validation and extension, e.g., indoor navigation service. This work would promote the development of ice navigation support system and potentially enhance the safety of ice navigation in the Arctic sea.

scholarly journals The anatomy of the anyplace indoor navigation service

2017 ◽  
Vol 9 (2) ◽  
pp. 3-10 ◽  
Author(s):  
Demetrios Zeinalipour-Yazti ◽  
Christos Laoudias
Keyword(s):  
Indoor Navigation ◽  
Navigation Service

Design of Continuous Indoor Navigation System Based on INS and Wifi

2013 ◽  
Vol 303-306 ◽  
pp. 2046-2049 ◽  
Author(s):  
Yi Hu ◽  
Lei Sheng ◽  
Shan Jun Zhang
Keyword(s):  
Navigation System ◽  
Smart Phone ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
High Price ◽  
Position Information ◽  
Positioning Method ◽  
Indoor Navigation System ◽  
Navigation Service

The application of navigation, such as guidance of pedestrians, requires a certain accuracy of continuous outdoor and indoor positioning. In outdoor environments GPS system has proved to be effective. However in indoor it is challenging to control the accuracy within 2 to 3 meters. At present several approaches have been developed for indoor positioning, such as RFID. But they are mainly been implemented in professional areas, for general user such as tourists and visual incapable users it is difficult to take advantage of these technologies because of the high price of terminal and the navigation service covered area is extremely limited. In this paper, a new approach of indoor navigation method is proposed to solve the problems of traditional methods. It is based on INS and wifi positioning technology. As hardware, wifi receiver, smart phone built-in accelerometer and digital compass are selected and investigated. User’s indoor position is first estimated by dead reckoning method with INS navigation system and then be recalibrated by wifi position information. Several experiments performed in the test verified the effectiveness of this indoor continuous positioning method described in this paper.

scholarly journals Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2981
Author(s):  
Haotai Sun ◽  
Xiaodong Zhu ◽  
Yuanning Liu ◽  
Wentao Liu
Keyword(s):  
Machine Learning ◽  
Radio Frequency ◽  
Indoor Navigation ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Dual Frequency ◽  
Evaluation Algorithm ◽  
Precision And Accuracy ◽  
Navigation Service

Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.

scholarly journals Towards an Automatic Ice Navigation Support System in the Arctic Sea

2021 ◽  
Author(s):  
Xintao Liu ◽  
Shahram Sattar ◽  
Songnian Li
Keyword(s):  
Support System ◽  
Indoor Navigation ◽  
The Arctic ◽  
Emergency Situations ◽  
Arctic Sea ◽  
Navigation Support ◽  
Northern Regions ◽  
Ice Navigation ◽  
Navigation Service ◽  
Sea Area

Conventional ice navigation in the sea is manually operated by well-trained navigators, whose experiences are heavily relied upon to guarantee the ship’s safety. Despite the increasingly available ice data and information, little has been done to develop an automatic ice navigation support system to better guide ships in the sea. In this study, using the vector-formatted ice data and navigation codes in northern regions, we calculate ice numeral and divide sea area into two parts: continuous navigable area and the counterpart numerous separate unnavigable area. We generate Voronoi Diagrams for the obstacle areas and build a road network-like graph for connections in the sea. Based on such a network, we design and develop a geographic information system (GIS) package to automatically compute the safest-and-shortest routes for different types of ships between origin and destination (OD) pairs. A visibility tool, Isovist, is also implemented to help automatically identify safe navigable areas in emergency situations. The developed GIS package is shared online as an open source project called NavSpace, available for validation and extension, e.g., indoor navigation service. This work would promote the development of ice navigation support system and potentially enhance the safety of ice navigation in the Arctic sea.

scholarly journals DEVELOPING A METHOD TO GENERATE INDOORGML DATA FROM THE OMNI-DIRECTIONAL IMAGE

2015 ◽  
Vol XL-2/W4 ◽  
pp. 17-19 ◽  
Author(s):  
M. Kim ◽  
J. Lee
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Information Service ◽  
Indoor Navigation ◽  
Classification Method ◽  
Indoor Space ◽  
Edge Detection Method ◽  
Navigation Service ◽  
Space Data ◽  
Directional Image

Recently, many applications for indoor space are developed. The most realistic way to service an indoor space application is on the omni-directional image so far. Due to limitations of positioning technology and indoor space modelling, however, indoor navigation service can’t be implemented properly. In 2014, IndoorGML is approved as an OGC’s standard. This is an indoor space data model which is for the indoor navigation service. Nevertheless, the IndoorGML is defined, there is no method to generate the IndoorGML data except manually. This paper is aimed to propose a method to generate the IndoorGML data semi-automatically from the omni-directional image. In this paper, image segmentation and classification method are adopted to generate the IndoorGML data. The edge detection method is used to extract the features from the image. After doing the edge detection method, image classification method with ROI is adopted to find the features that we want. The following step is to convert the extracted area to the point which is regarded as state and connect to shooting point’s state. This is the IndoorGML data at the shooting point. It can be expanded to the floor’s IndoorGML data by connecting the each shooting points after repeating the process. Also, IndoorGML data of building can be generated by connecting the floor’s IndoorGML data. The proposed method is adopted at the testbed, and the IndoorGML data is generated. By using the generated IndoorGML data, it can be applied to the various applications for indoor space information service.

Indoor navigation service based on Wi-Fi positioning

2017 ◽  
Author(s):  
D.V. Gmar' ◽  
K.I. Dyuldina ◽  
S.I. Snopko ◽  
K.J. Shakhgeldyan ◽  
V.V. Kryukov
Keyword(s):  
Indoor Navigation ◽  
Navigation Service

An Indoor Navigation Service Robot System Based on Vibration Tactile Feedback

2017 ◽  
Vol 9 (3) ◽  
pp. 331-341 ◽  
Author(s):  
Huang Peng ◽  
Guangming Song ◽  
Jian You ◽  
Ying Zhang ◽  
Jie Lian
Keyword(s):  
Tactile Feedback ◽  
Indoor Navigation ◽  
Service Robot ◽  
Robot System ◽  
Navigation Service

scholarly journals Ontology based Indoor Navigation Service for the ILONA System

2018 ◽  
pp. 20-29
Author(s):  
Dániel Péter Kun ◽  
Erika Baksáné Varga ◽  
Zsolt Tóth
Keyword(s):  
Shortest Path ◽  
Indoor Environment ◽  
Real Life ◽  
Indoor Navigation ◽  
Graph Search ◽  
Search Problem ◽  
Shortest Path Algorithm ◽  
Complex Queries ◽  
Way Finding ◽  
Navigation Service

An ontology based way finding algorithm is presented in this paper that allows route generation between two separate parts of an indoor environment. The presented ontology provides a flexible way to describe and model the indoor environment, in addition it fits and extends the existing model of the ILONA System. Ontology reasoners provide an efficient way to perform complex queries over the knowledge base. The instances, that are queried by the reasoner, are used to initialize the graph which represents an indoor environment. Due to parameterization of the reasoner, different graphs can be generated from the ontology which makes the way finding algorithm flexible. Thus, the task of indoor way finding was converted into a well-known graph search problem. Dijkstra’s shortest path algorithm is used for route generation in the graph yielded. The algorithm was implemented and tested in the ILONA System and its functioning is demonstrated by real-life scenarios.

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