Localization and Mapping for Indoor Navigation

Gaber, Heba; Marey, Mohamed; Amin, Safaa; Tolba, Mohamed F.

doi:10.4018/978-1-5225-2229-4.ch007

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Another approach is Simultaneous Localization and Mapping (SLAM), where a system must have the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. SLAM has proven to be an effective method for indoor navigation [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

On-Device Learning of Indoor Location for WiFi Fingerprint Approach

Herrera-Rivas¹,

Torres-Huitzil

Marin-Castro

et al. 2018

Sensors

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Indoor positioning is a recent technology that has gained interest in industry and academia thanks to the promising results of locating objects, people or robots accurately in indoor environments. One of the utilized technologies is based on algorithms that process the Received Signal Strength Indicator (RSSI) in order to infer location information without previous knowledge of the distribution of the Access Points (APs) in the area of interest. This paper presents the design and implementation of an indoor positioning mobile application, which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from proposed indoor scenarios. Then, from the tested classifiers in the early stage, only the top-five classifiers were integrated with the proposed mobile indoor positioning, based on the accuracy obtained for the test scenarios. The proposed indoor application achieves high classification rates, above 89%, for at least 10 different locations in indoor environments, where each location has a minimum separation of 0.5 m.

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Section: Introductionmentioning

confidence: 99%

On-Device Learning of Indoor Location for WiFi Fingerprint Approach

Herrera-Rivas¹,

Torres-Huitzil

Marin-Castro

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

Content Based Image Retrieval Using Local Feature Descriptors on Hadoop for Indoor Navigation

Gaber

Marey

Amin

et al. 2018

Advances in Intelligent Systems and Computing

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Conflict Avoidance in Social Navigation—a Survey

Mirsky,

Xiao,

Hart

et al. 2024

J. Hum.-Robot Interact.

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A major goal in robotics is to enable intelligent mobile robots to operate smoothly in shared human-robot environments. One of the most fundamental capabilities in service of this goal is competent navigation in this “social” context. As a result, there has been a recent surge of research on social navigation; and especially as it relates to the handling of conflicts between agents during social navigation. These developments introduce a variety of models and algorithms, however as this research area is inherently interdisciplinary, many of the relevant papers are not comparable and there is no shared standard vocabulary. This survey aims to bridge this gap by introducing such a common language, using it to survey existing work, and highlighting open problems. It starts by defining the boundaries of this survey to a limited, yet highly common type of social navigation – conflict avoidance. Within this proposed scope, this survey introduces a detailed taxonomy of the conflict avoidance components. This survey then maps existing work into this taxonomy, while discussing papers using its framing. Finally, this paper proposes some future research directions and open problems that are currently on the frontier of social navigation to aid ongoing and future research.

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Localization and Mapping for Indoor Navigation

Cited by 3 publications

References 24 publications

On-Device Learning of Indoor Location for WiFi Fingerprint Approach

On-Device Learning of Indoor Location for WiFi Fingerprint Approach

Content Based Image Retrieval Using Local Feature Descriptors on Hadoop for Indoor Navigation

Conflict Avoidance in Social Navigation—a Survey

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