Fast localization in indoor environments

Elias, Rimon; El-Nahas, Amal

doi:10.1109/cisda.2009.5356553

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…In [7] and [6], Elias and Elnahas propose a fast localization approach in indoor environments. Their work is based on using 2D JUDOCA features for localizing a user roaming inside a building wearing a camera-phone.…”

Section: Related Workmentioning

confidence: 99%

“…Recent research in indoor and outdoor place recognition and robot localization is represented by the work reported in [17], [18], [11], [7] and [6]. Wu et al [17] have proposed a technique based on viewpoint invariant patches (VIP) that are extracted from orthogonal projections of 3D textures obtained from dense 3D reconstruction of a scene using Structure from Motion (SfM).…”

Section: Related Workmentioning

confidence: 99%

“…In contrast with the place recognition work in outdoor environments, place recognition research in indoor environments has received relatively little attention [7]. The indoor problems are more daunting because we do not have access to geo-tagged resource of images such as the satellite or Internet image databases for outdoors.…”

Section: Introductionmentioning

confidence: 96%

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An approach-path independent framework for place recognition and mobile robot localization in interior hallways

Yousef

Park

Kak

2013

2013 IEEE International Conference on Robotics and Automation

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Our work provides a fast approach-pathindependent framework for the problem of place recognition and robot localization in indoor environments. The approachpath independence is achieved by using highly viewpointinvariant 3D junction features extracted from stereo pairs of images; these are based on stereo reconstructions of the JUDOCA junctions extracted from the individual images of a stereo pair. The speed in place-recognition and robot-localization is achieved by using a novel cylindrical data structure -we refer to it as the Feature Cylinder -for representing either all of the 3D junction features found in a hallway system during the learning phase of the robot or a set of locale signatures derived from the data. For the case when all data is placed on the Feature Cylinder, we can use the 3D-POLY polynomial-time in a hypothesize-and-verify approach to place recognition. On the other hand, in the locale signature based approach, we can use the same data structure for constant-time place recognition.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An approach-path independent framework for place recognition and mobile robot localization in interior hallways

Yousef

Park

Kak

2013

2013 IEEE International Conference on Robotics and Automation

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“…There have been several attempts to obtain indoor localization through image matching techniques [10,6], but indoor environments are extremely challenging for this kind of application, and even if it is possible to recognize the shapes of pedestrians and objects through image processing techniques, the identification of similar ones is difficult for a computer vision-based system intended to monitor whole buildings [2]. In fact, the computational cost of high-dimensional feature extraction and processing is typically prohibitive for implementation in a cell-phone [8].…”

Section: Location-based Approaches: Gps and Indoor Localizationmentioning

confidence: 99%

Opportunistic strategies for lightweight signal processing for body sensor networks

Seto

Martin

Yang

et al. 2010

Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

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We present a mobile platform for body sensor networking based on a smartphone for lightweight signal processing of sensor mote data. The platform allows for local processing of data at both the sensor mote and smartphone levels, reducing the overhead of data transmission to remote services. We discuss how the smartphone platform not only provides the ability for wearable signal processing, but it allows for opportunistic sensing strategies, in which many of the onboard sensors and capabilities of modern smartphones may be collected and fused with body sensor data to provide environmental and social context. We propose that this can help refine data reduction at the local level. We describe three examples related to health and wellness, to which our system has been applied.

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