Indoor/Outdoor Mobile Navigation via Knowledge-Based POI Discovery in Augmented Reality

Ruta, Michele; Scioscia, Floriano; Ieva, Saverio; Filippis, Danilo De; Sciascio, Eugenio Di

doi:10.1109/wi-iat.2015.243

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…Previous AR attempts where only designed for people with ability to walk and manipulate a smartphone touch screen (Koch et al, 2014;Neges et al, 2015;Ruta et al, 2015). However, due to their limitations, wheelchair users move around the environment in an imaginary line that is below healthy people's eyes.…”

Section: Discussionmentioning

confidence: 99%

Mobile Augmented Reality enhances indoor navigation for wheelchair users

et al. 2016

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Introduction: Individuals with mobility impairments associated with lower limb disabilities often face enormous challenges to participate in routine activities and to move around various environments. For many, the use of wheelchairs is paramount to provide mobility and social inclusion. Nevertheless, they still face a number of challenges to properly function in our society. Among the many difficulties, one in particular stands out: navigating in complex internal environments (indoors). The main objective of this work is to propose an architecture based on Mobile Augmented Reality to support the development of indoor navigation systems dedicated to wheelchair users, that is also capable of recording CAD drawings of the buildings and dealing with accessibility issues for that population. Methods: Overall, five main functional requirements are proposed: the ability to allow for indoor navigation by means of Mobile Augmented Reality techniques; the capacity to register and configure building CAD drawings and the position of fiducial markers, points of interest and obstacles to be avoided by the wheelchair user; the capacity to find the best route for wheelchair indoor navigation, taking stairs and other obstacles into account; allow for the visualization of virtual directional arrows in the smartphone displays; and incorporate touch or voice commands to interact with the application. The architecture is proposed as a combination of four layers: User interface; Control; Service; and Infrastructure. A proof-ofconcept application was developed and tests were performed with disable volunteers operating manual and electric wheelchairs. Results: The application was implemented in Java for the Android operational system. A local database was used to store the test building CAD drawings and the position of fiducial markers and points of interest. The Android Augmented Reality library was used to implement Augmented Reality and the Blender open source library handled the basis for implementing directional navigation arrows. OpenGL ES provided support for various graphics and mathematical transformations for embedded systems, such as smartphones. Experiments were performed in an academic building with various labs, classrooms and male and female bathrooms. Two disable volunteers using wheelchairs showed no difficulties to interact with the application, either by entering touch or voice commands, and to navigate within the testing environment with the help of the navigational arrows implemented by the augmented reality modules. Conclusion: The novel features implemented in the proposed architecture, with special emphasis on the use of Mobile Augmented Reality and the ability to identify the best routes free of potential hazards for wheelchair users, were capable of providing significant benefits for wheelchair indoor navigation when compared to current techniques described in the literature.

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Section: Discussionmentioning

confidence: 99%

Mobile Augmented Reality enhances indoor navigation for wheelchair users

et al. 2016

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“…Indoor navigation models integrate the acquired data with OSM by using various algorithms to provide navigation inside complex buildings. The research trends on devising methods for indoor navigation using augmented reality [179][180][181][182] and 3D models [183,184] have also been observed during the period 2012-2016.…”

Section: Indoor Navigation Models (T55)mentioning

confidence: 99%

“…Another potential research and application area is indoor navigation. Traditional and contemporary algorithms [65,66,68,115] are being supported by augmented reality [179][180][181][182] and 3D models [183,184] for this purpose. The current study suggests that there are several opportunities to work upon the following:…”

Section: Indoor Navigation Modelsmentioning

confidence: 99%

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Sehra

Singh

2017

IJGI

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OpenStreetMap (OSM), based on collaborative mapping, has become a subject of great interest to the academic community, resulting in a considerable body of literature produced by many researchers. In this paper, we use Latent Semantic Analysis (LSA) to help identify the emerging research trends in OSM. An extensive corpus of 485 academic abstracts of papers published during the period 2007-2016 was used. Five core research areas and fifty research trends were identified in this study. In addition, potential future research directions have been provided to aid geospatial information scientists, technologists and researchers in undertaking future OSM research.

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“…For instance, [16] proposed an store recommendation systems by mining the context of decision-making behaviour using eye-tracking data, [17] proposed a POI discovery approach by matching the user profile and the semantic-enhanced POIs, [18] proposed a recommended system to help users in shopping for technical products by considering user preference and technical product attributes, and [19] proposed an automatic mobile assistant for museum visiting based on WiFi-based indoor positioning. Additionally, Shin et al [20] constructed an indoor database platform for indoor location-based services.…”

Section: Indoor Poi Recommendationmentioning

confidence: 99%

“…(2) We only use the indoor trajectories to learn user's preferences for making recommendations, unlike literatures [17,18] that need additional user profiles for recommendation. (3) Existing indoor POI recommendation algorithms, such as [7][8][9][10], merely use user-based or item-based collaborative filtering for making recommendation and will suffer data sparsity problem since numerous users only have few checkin information.…”

Section: Indoor Poi Recommendationmentioning

confidence: 99%

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

Zheng

Chen

et al. 2017

Mobile Information Systems

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Indoor shopping trajectories provide us with a new approach to understanding user's behaviour pattern in urban shopping mall, which can be derived from user-generated WiFi logs using indoor localization technology. In this paper, we propose a locationaware Point-of-Interest (POI) recommendation service in urban shopping mall that offers a user a set of indoor POIs by considering both personal interest and location preference. The POI recommendation service cannot only improve user's shopping experience but also help the store owner better understand user's shopping preference and intent. Specifically, the proposed method consists of two phases: offline modelling and online recommendation. The offline modelling phase is designed to learn user preference by mining his/her historical shopping trajectories. The online recommendation phase automatically produces top-recommended POIs based on the learnt preference. To demonstrate the utility of our proposed approach, we have performed a comprehensive experiment evaluation on a real-world dataset collected by 468 users over 33 days. The experimental results show that the proposed recommendation service achieves much better recommendation performance than several existing benchmark methods.

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Indoor/Outdoor Mobile Navigation via Knowledge-Based POI Discovery in Augmented Reality

Cited by 19 publications

References 11 publications

Mobile Augmented Reality enhances indoor navigation for wheelchair users

Mobile Augmented Reality enhances indoor navigation for wheelchair users

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

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