Indoor localization and navigation using smartphones augmented reality and inertial tracking

Delail, Buti Al; Weruaga, Luis; Zemerly, Mohamed Jamal; Ng, Jason

doi:10.1109/icecs.2013.6815564

Cited by 21 publications

(11 citation statements)

References 19 publications

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“…Unlike marker-based methods, 2D image recognition-based methods search for pre-annotated objects, such as billboards, trademarks, and signs in the environment, and then determine the user’s location based on the obtained objects [ 5 , 6 , 12 , 13 , 14 , 15 ]. With the development of image recognition technology, there is a trend for replacing marker-based navigation with image recognition-based navigation.…”

Section: Related Workmentioning

confidence: 99%

ARBIN: Augmented Reality Based Indoor Navigation System

Huang

Hsu

Chu

et al. 2020

Sensors

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Due to the popularity of indoor positioning technology, indoor navigation applications have been deployed in large buildings, such as hospitals, airports, and train stations, to guide visitors to their destinations. A commonly-used user interface, shown on smartphones, is a 2D floor map with a route to the destination. The navigation instructions, such as turn left, turn right, and go straight, pop up on the screen when users come to an intersection. However, owing to the restrictions of a 2D navigation map, users may face mental pressure and get confused while they are making a connection between the real environment and the 2D navigation map before moving forward. For this reason, we developed ARBIN, an augmented reality-based navigation system, which posts navigation instructions on the screen of real-world environments for ease of use. Thus, there is no need for users to make a connection between the navigation instructions and the real-world environment. In order to evaluate the applicability of ARBIN, a series of experiments were conducted in the outpatient area of the National Taiwan University Hospital YunLin Branch, which is nearly 1800 m2, with 35 destinations and points of interests, such as a cardiovascular clinic, x-ray examination room, pharmacy, and so on. Four different types of smartphone were adopted for evaluation. Our results show that ARBIN can achieve 3 to 5 m accuracy, and provide users with correct instructions on their way to the destinations. ARBIN proved to be a practical solution for indoor navigation, especially for large buildings.

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Section: Related Workmentioning

confidence: 99%

ARBIN: Augmented Reality Based Indoor Navigation System

Huang

Hsu

Chu

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…An approach using a cloud of interest (COI), was proposed maximizing the information that the user is receiving without context distortion, too much cluttering, and additional cognitive task of looking through multiple views at the same time and still be suitable for small viewing spaces, hence bridging the challenge of creating models for mobile 3D geovirtual environments [4]. As 3D visualization of POI in the indoor environment representing both objects and spaces are emphasized [49], this emphasizes the need for a formalized data model that enables linking with other data.…”

Section: Related Researchmentioning

confidence: 99%

Developing a Data Model of Indoor Points of Interest to Support Location-Based Services

Claridades

Lee

2020

Journal of Sensors

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Focus on indoor spatial applications has been rising with the growing interest in indoor spaces. Along with the widespread use of mobile devices and the internet, it has increased demands for indoor location-based services (LBS), demanding more efficient representation and management of indoor spatial data. Indoor points of interest (Indoor POI) data, representing both spaces and facilities located indoors, provide the infrastructure for these services. These datasets are vital in delivering timely and accurate information to users, such as in cases of managing indoor facilities. However, even though there are studies that explore its use across applications and efforts exerted towards the standardization of the data model, most POI development studies have focused on the outdoors and remain underdeveloped in the indoors. In this paper, we propose a spatial-temporal Indoor POI data model to provide direction for the establishment of indoor POI data and to address limitations in currently available data specifications. By exploring how different Indoor POIs are from its outdoor counterparts, particularly on extending its outdoor counterparts’ functions on searching, sharing, and labeling, we describe the data model and its components using the Unified Modeling Language (UML). We perform an SQL-based query experiment to demonstrate the potential use of the data model using sample data.

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“…Radio frequency identification (RFID) demands sufficient device setup and can be labor-intensive. On the other hand, researches have demonstrated the AR navigation system to be superior and possess great potential compared to using a traditional map [ 26 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

MARINS: A Mobile Smartphone AR System for Pathfinding in a Dark Environment

Diao¹,

Shih²

2018

Sensors

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Traditional egress routes are normally indicated on floor plans and function as designed, assuming that people can identify their relative location and orientation. However, the evacuation process can easily become complicated in a dark or hazardous environment with potential blockage of unexpected obstacles. This study developed the mobile AR indoor navigation system (MARINS) using a smartphone as a device to guide users to exits in a 0-lux setting with the path only illuminated by the phone camera’s LED. The system is developed using Apple ARKit SDK with the associated simultaneous localization and mapping (SLAM) function on a Unity platform in four modules. A maze scenario is planned in an environment built by carton walls. Time and distance traveled by the experimental group and the control group are measured. The results of statistical analysis demonstrate that the MARINS system can reduce travel time in known space and in total summation compared to the application of a traditional map. The system also reduces travel distance and misjudgments with higher system usability than the application of a traditional map.

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Indoor localization and navigation using smartphones augmented reality and inertial tracking

Cited by 21 publications

References 19 publications

ARBIN: Augmented Reality Based Indoor Navigation System

ARBIN: Augmented Reality Based Indoor Navigation System

Developing a Data Model of Indoor Points of Interest to Support Location-Based Services

MARINS: A Mobile Smartphone AR System for Pathfinding in a Dark Environment

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