Indoor navigation assistance with a Smartphone camera based on vanishing points

Elloumi, Wael; Guissous, Kamel; Chetouani, Aladine; Canals, Raphaël; Leconge, Rémy; Emile, Bruno; Treuillet, Sylvie

doi:10.1109/ipin.2013.6817911

Cited by 37 publications

(27 citation statements)

References 23 publications

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“…The main disadvantage of the video technology lies in the cost, which is still too high, especially for systems with very high precision. For this reason, with the spread of mobile devices, the interest in indoor location systems using smartphones equipped with video camera ( [10,11]) is increased. The infrared (IR) technology is also widely used for indoor localization, as shown in [12,13], though multipath effect drastically reduces the localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

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An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

Almeida

Fiore

Mainetti

et al. 2017

Wireless Communications and Mobile Computing

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The world population will be made up of a growing number of elderly people in the near future. Aged people are characterized by some physical and cognitive diseases, like mild cognitive impairment (MCI) and frailty, that, if not timely diagnosed, could turn into more severe diseases, like Alzheimer disease, thus implying high costs for treatments and cares. Information and Communication Technologies (ICTs) enabling the Internet of Things (IoT) can be adopted to create frameworks for monitoring elderly behavior which, alongside normal clinical procedures, can help geriatricians to early detect behavioral changes related to such pathologies and to provide customized interventions. As part of the City4Age project, this work describes a novel approach for collecting and managing data about elderly behavior during their normal activities. The data capturing layer is an unobtrusive and low-cost sensing infrastructure abstracting the heterogeneity of physical devices, while the data management layer easily manages the huge quantity of sensed data, giving them semantic meaning and fostering data shareability. This work provides a functional validation of the proposed architecture and introduces how the data it manages can be used by the whole City4Age platform to early identify risks related to MCI/frailty and promptly intervene.

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Section: Related Workmentioning

confidence: 99%

“…"sensorId": "sensorId:beacon_123456" (8) }, (9) "timestamp": "2017-01-22 07:08:41.22222", (10) "rating": 0.4, (11) "extra": { (12)…”

Section: Stress Testmentioning

confidence: 99%

An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

Almeida

Fiore

Mainetti

et al. 2017

Wireless Communications and Mobile Computing

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“…These solutions mainly rely on external markers [4], others require laborious setups (taking reference frames every 150cm) [2], rely on other modalities like WiFi [7], are computationally intensive (10s per frame) [3].…”

Section: Related Workmentioning

confidence: 99%

Robust vision-based indoor localization

Clark

Trigoni

Markham

2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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Vision-based positioning has proven to be highly successful and popular in mobile robotics and computer vision applications. These methods have, however, not enjoyed the same popularity in the field of indoor localization.In this work we highlight some of the issues that arise when using vision-based methods for indoor localization. We then propose means of addressing these issues and implement a proof-of-concept visual inertial odometry system for a mobile device.Preliminary experiments have been carried out in a small library where sub-meter positioning accuracy was attained. Based on our proof-of-concept, we believe that visual inertial odometry techniques can provide the levels of positioning accuracy needed for widespread adoption.

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“…Our solution relies exclusively on the smartphone resources and provides instantaneous localization and orientation estimation (6 DoF). To overcome the drift problem (cumulative errors over time) revealed by our previous feasibility study [1], the pedestrian's position is obtained by matching the current image produced by the smartphone camera with geo-referenced images extracted from a database while their orientation is estimated thanks to the 3 orthogonal directions of the vanishing points (VP). The estimated position is then refined by using an Extended Kalman Filter (EKF).…”

Section: Introductionmentioning

confidence: 99%

Indoor Pedestrian Localization With a Smartphone: A Comparison of Inertial and Vision-Based Methods

Elloumi¹,

Latoui²,

Canals³

et al. 2016

IEEE Sensors J.

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Indoor pedestrian navigation systems are increasingly needed in various types of applications. However, such systems are still face many challenges. In addition to being accurate, a pedestrian positioning system must be mobile, cheap, and lightweight. Many approaches have been explored. In this paper, we take the advantage of sensors integrated in a smartphone and their capabilities to develop and compare two low-cost, hands-free, and handheld indoor navigation systems. The first one relies on embedded vision (smartphone camera), while the second option is based on low-cost smartphone inertial sensors (magnetometer, accelerometer, and gyroscope) to provide a relative position of the pedestrian. The two associated algorithms are computationally lightweight, since their implementations take into account the restricted resources of the smartphone. In the experiment conducted, we evaluate and compare the accuracy and repeatability of the two positioning methods for different indoor paths. The results obtained demonstrate that the visionbased localization system outperforms the inertial sensor-based positioning system.

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Indoor navigation assistance with a Smartphone camera based on vanishing points

Cited by 37 publications

References 23 publications

An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

Robust vision-based indoor localization

Indoor Pedestrian Localization With a Smartphone: A Comparison of Inertial and Vision-Based Methods

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