Location Fixing and Fingerprint Matching Fingerprint Map Construction for Indoor Localization

Abstract

Building the fingerprint map for indoor localization problems is a labour-intensive and time-consuming process. However, due to its direct influence on the location estimation accuracy, finding a proper mechanism to construct the fingerprint map is essential to enhance the position estimation accuracy. Therefore, in this work, we present a fingerprint map construction technique based on location fix determination and fingerprint matching motivated by the availability of advanced sensing capabilities in smartphones to reduce the time and labour cost required for the site survey. The proposed Location Fixing and Finger Matching (LFFM) method use a landmark graph-based localization approach to automatically estimate the location fixes for the Reference Points and matching the collected fingerprints, without requiring active user participation. Experimental results show that the proposed LFFM is faster than the manual fingerprint map construction method and remarkably improves the positioning accuracy.

A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired

“…Accelerometers can be used to determine changes in the user's position when acceleration in each direction is detected. This is a very rough estimate, which can be improved using a gyroscope to observe changes in a course [7,90,91]. The evidence of initial acceleration can be confirmed by the fact that a user is walking, recognizing (also with an accelerometer) the typical tremor associated with walking [86].…”

Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

“…LiFS [102] creates the radio map by using smartphone built-in sensors for the floor plan, which results in quicker implementation and fewer working hours. Another crowdsourcing scheme [103] is an RP graph-based approach that uses sensors from smart devices to dynamically locate the position of RPs linked to the captured fingerprints and build realistic, quick, and accurate fingerprints. In this scheme, the element of confidence ("belief factor") in good precision and reliability is used.…”

A Systematic Review of Location Aware Schemes in the Internet of Things

“…In this section, we discuss the open research challenges and possible future directions in this domain. Environment Independence —The majority of studies focus on either indoor [ 87 , 88 , 89 , 90 ] or outdoor localization [ 91 , 92 , 93 ]. This environment-centric application’s nature limits the applicability and widespread use in real-life scenarios.…”