Smartphone Sensor Reliability for Augmented Reality Applications

Blum, Jeffrey R.; Greencorn, Daniel G.; Cooperstock, Jeremy R.

doi:10.1007/978-3-642-40238-8_11

Cited by 54 publications

(41 citation statements)

References 17 publications

(17 reference statements)

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“…A detailed overview of mobile device sensors and their calibration, error sources and error margins is available in the literature (Blum et al, 2013).…”

Section: Mobile Sensor Orientation Estimatementioning

confidence: 99%

Direct Image-to-Geometry Registration Using Mobile Sensor Data

Kehl¹,

Buckley²,

Gawthorpe³

et al. 2016

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

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ABSTRACT:Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration-and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm's accuracy compared to traditional methods, and the impact of computational-and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.

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“…A detailed overview of mobile device sensors and their calibration, error sources and error margins is available in the literature (Blum et al, 2013).…”

Section: Mobile Sensor Orientation Estimatementioning

confidence: 99%

Direct Image-to-Geometry Registration Using Mobile Sensor Data

Kehl¹,

Buckley²,

Gawthorpe³

et al. 2016

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

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“…[44]. In a detailed experiment on the accuracy of GPS sensors installed on mobile devices, Blum et al show that the location is reported with a precision varying from 10 to 60 meters, depending on the device orientation and type, and, in cities, on the surrounding buildings [3]. Hence, when designing a system based on mobile devices it would reasonable to consider regions with sides tens of meters long.…”

Section: Spatial Representationmentioning

confidence: 99%

Location privacy without mutual trust: The spatial Bloom filter

Calderoni

Palmieri

Maio

2015

Computer Communications

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Access to the full text of the published version may require a subscription. Rights AbstractLocation-aware applications are one of the biggest innovations brought by the smartphone era, and are effectively changing our everyday lives. But we are only starting to grasp the privacy risks associated with constant tracking of our whereabouts. In order to continue using location-based services in the future without compromising our privacy and security, we need new, privacy-friendly applications and protocols. In this paper, we propose a new compact data structure based on Bloom filters, designed to store location information. The Spatial Bloom Filter (SBF), as we call it, is designed with privacy in mind, and we prove it by presenting two private positioning protocols based on the new primitive. The protocols keep the user's exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user. The two proposed protocols are aimed at different scenarios: a two-party setting, in which communication happens directly between the user and the service provider, and a three-party setting, in which the service provider outsources to a third party the communication with the user. A detailed evaluation of the efficiency and security of our solution shows that privacy can be achieved with minimal computational and communication overhead. The potential of spatial Bloom filters in terms of generality, security and compactness makes them ready for deployment, and may open the way for privacy preserving location-aware applications.

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“…2 Paul sends to Ursula the encryption of the precomputed SBF Enc b # , the set of k hash functions H, the value m and the conventional grid E. 3 At regular time intervals, or when required by the specific application, Ursula determines her geographic position and selects the corresponding grid region eu ∈ E. Then, following Algorithm 2 and using the values and functions shared by Paul, she builds a spatial Bloom filter b # u over {eu} and counts the number z of values equal to 1 therein. 4 Ursula computes e # = Enc b # b # u using the homomorphic properties of the encryption scheme (Algorithm 1).…”

Section: Two-party Scenariomentioning

confidence: 99%

“…2 Paul sends to Olga the encryption of the precomputed spatial Bloom filter Enc b # and the value m. Then, Paul sends to the user Ursula the set of k hash functions H and the conventional grid E. 3 At regular time intervals, or when required by the specific application, Ursula determines her geographic position and selects the corresponding grid region eu ∈ E. Then, she computes the values {v1, . .…”

Section: Security Analysismentioning

confidence: 99%

Spatial Bloom Filters: Enabling Privacy in Location-Aware Applications

Palmieri

Calderoni

Maio

2015

Information Security and Cryptology

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Abstract. The wide availability of inexpensive positioning systems made it possible to embed them into smartphones and other personal devices. This marked the beginning of location-aware applications, where users request personalized services based on their geographic position. The location of a user is, however, highly sensitive information: the user's privacy can be preserved if only the minimum amount of information needed to provide the service is disclosed at any time. While some applications, such as navigation systems, are based on the users' movements and therefore require constant tracking, others only require knowledge of the user's position in relation to a set of points or areas of interest. In this paper we focus on the latter kind of services, where location information is essentially used to determine membership in one or more geographic sets. We address this problem using Bloom Filters (BF), a compact data structure for representing sets. In particular, we present an extension of the original Bloom filter idea: the Spatial Bloom Filter (SBF). SBF's are designed to manage spatial and geographical information in a space efficient way, and are well-suited for enabling privacy in location-aware applications. We show this by providing two multi-party protocols for privacy-preserving computation of location information, based on the known homomorphic properties of public key encryption schemes. The protocols keep the user's exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user.

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Smartphone Sensor Reliability for Augmented Reality Applications

Cited by 54 publications

References 17 publications

Direct Image-to-Geometry Registration Using Mobile Sensor Data

Direct Image-to-Geometry Registration Using Mobile Sensor Data

Location privacy without mutual trust: The spatial Bloom filter

Spatial Bloom Filters: Enabling Privacy in Location-Aware Applications

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