A motion tracking solution for indoor localization using smartphones

Abstract

As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge

Multi-Modal Probabilistic Indoor Localization on a Smartphone

“…Almost all smartphone-based systems leverage IMU data to improve performance. Since IMUs provide an estimate of the device's relative movement, both in terms of travelled distance and movement direction, integration can be applied to obtain position estimates; however, double integration of accelerometer data is notoriously inaccurate and step detection combined with estimated step length and direction is more commonly used [10], [21], [37].…”

“…For learning-based approaches, fingerprinting has seen widespread use. For example, Guimarães et al [10] use WiFi fingerprint maps for coarse location estimation, and refine the estimate using magnetic fingerprints and IMU measurements. Shu et al [29] use the same measurement modalities, but combine them in a particle filter.…”

“…These fingerprints are able to capture stationary non-line-ofsight (NLOS) and multipath effects. In the online localization phase, the recorded signal is matched with the fingerprint database, andafter possible combination with IMU data and map constraints-a position estimate is inferred [10], [29], [34].…”

Reduce RSSI Variance for Indoor Localization System Using Frequency Analysis

“…Calculate Euclidean distance, as in (1). (1) Then, choose the location at minimum distance, as in (2).…”

Smartphone-Based Indoor Localization within a 13th Century Historic Building

“…For this reason, some alternative approaches were presented to speed up the offline-phase. In [31] the positions of recorded references are interpolated between the start and end of some reference path, based on the pedestrians gait cycle. Unrecorded positions are then obtained using the flood fill algorithm.…”