Sensors for Indoor Mapping and Navigation

Khoshelham, Kourosh; Zlatanova, Sisi

doi:10.3390/s16050655

Cited by 27 publications

(13 citation statements)

References 52 publications

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“…WiFi Positioning Systems are mostly founded on the fingerprinting technique [16,17]. The Received Signal Strength Indication (RSSI) is used to generate a map of the environment with 2D coordinates and the values of signals received by different Access Points (APs), such as routers.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Domingo

Gómez-García-Bermejo

Zalama

et al. 2019

Sensors

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This work presents an integrated Indoor Positioning System which makes use of WiFi signals and RGB cameras, such as surveillance cameras, to track and identify people navigating in complex indoor environments. Previous works have often been based on WiFi, but accuracy is limited. Other works use computer vision, but the problem of identifying concrete persons relies on such techniques as face recognition, which are not useful if there are many unknown people, or where the robustness decreases when individuals are seen from different points of view. The solution presented in this paper is based on an accurate combination of smartphones along with RGB cameras, such as those used in surveillance infrastructures. WiFi signals from smartphones allow the persons present in the environment to be identified uniquely, while the data coming from the cameras allow the precision of location to be improved. The system is nonintrusive, and biometric data about subjects is not required. In this paper, the proposed method is fully described and experiments performed to test the system are detailed along with the results obtained.

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Section: Overview Of Related Workmentioning

confidence: 99%

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Domingo

Gómez-García-Bermejo

Zalama

et al. 2019

Sensors

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“…Indoor navigation is attracting the attention of many researchers in the last decade (Khoshelham and Zlatanova 2016). Indoor environments are usually much more complex and difficult for orientation compare to outdoor.…”

Section: Introductionmentioning

confidence: 99%

Navigation in Indoor Voxel Models

Gorte¹,

Zlatanova²,

Fadli

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The paper proposes to use voxel models of building interiors to perform indoor navigation. The algorithms can be purely geometrical, not relying on semantic information about different building elements, such as floors, walls, stairways etc. Therefore, it is possible to use voxel models from different data sources, in addition to vector-to-raster conversions. The paper demonstrates this on the basis of tree different input types: hand measurements, point clouds and images of floorplans. On the basis of these models, the paper shows how to determine the navigable space in a voxel model for a pedestrian actor, and how to compute paths from arbitrary sources to specified destinations.</p>

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“…Typically, these systems are used for assistance of disabled or aged people, robot path planning, AR gaming, tourist's guidance, and training [1,2]. Indoor navigation systems are aimed with either infrastructure-dependent systems [3,4] which use sensors embedded in the environment for user tracking or infrastructure-independent systems [5]. Typically, a user navigating in an indoor environment needs two types of information including his/her own position and a path toward his/her specific destination [5].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of these systems have the common limitations including high cost, low accuracy, signals issues, difficult to install and use, specific to a particular building (not generic) and not extendable. For example, infrastructure-dependent systems [3,4] demand for a high cost. Similarly, cascaded deep neural network (CDNN) [10] suffers from computational complexity and minimal accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Generic Approach toward Indoor Navigation and Pathfinding with Robust Marker Tracking

2019

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Indoor navigation and localization has gained a key attention of the researchers in the recent decades. Various technologies such as WiFi, Bluetooth, Ultra Wideband (UWB), and Radio-frequency identification (RFID) have been used for indoor navigation and localization. However, most of these existing methods often fail in providing a reasonable solution to the key challenges such as implementation cost, accuracy and extendibility. In this paper, we proposed a low-cost, and extendable framework for indoor navigation. We used simple markers printed on the paper, and placed on ceilings of the building. These markers are detected by a smartphone’s camera, and the audio and visual information associated with these markers are used as a user guidance. The system finds shortest path between any two arbitrary nodes for user navigation. In addition, it is extendable having the capability to cover new sections by installing new nodes at any place in the building. The system can be used for guidance of the blind people, tourists and new visitors in an indoor environment. The evaluation results reveal that the proposed system can guide users toward their destination in an efficient and accurate manner.

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Sensors for Indoor Mapping and Navigation

Abstract: With the growth of cities and increased urban population there is a growing demand for spatial information of large indoor environments.[...]

Cited by 27 publications

References 52 publications

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Navigation in Indoor Voxel Models

A Generic Approach toward Indoor Navigation and Pathfinding with Robust Marker Tracking

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