Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

Abstract

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

“…Since these results depend mainly on Wi-Fi fingerprinting, the performance might be affected by the presence of high human mobility in the test area [56]. Additionally, the overall performance of the standalone positioning scenario can be improved by augmenting the solution with other techniques, such as the geomagnetic field anomalies or visual scene recognition [57][58][59][60]. However, the main objective of the standalone filter in this work is to form the performance baseline, to which the effect of collaboration between nodes is to be measured, as discussed later in Section 4.2.3.…”

Sensor Modalities and Fusion for Robust Indoor Localisation

“…Due to the emergence of Artificial Neural Networks (ANN) in the recent years, a number of researchers have considered the use of a tailored network for sensor fusion. Most of the approaches use Deep Neural Networks (DNN) [84,94,161]. While there exists a body of literature dedicated to objectivespecific fusion methods using ANN [151,154,155,176], there is an evident lack of standarisation between the positioning methods, and it still remains largely unexplored.…”

Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

“…Authors in [61] present a system which utilizes the magnetic field data for localization using a smartphone. The proposed method is based on the smartphone sensors and utilizes the Wi-Fi and magnetic fingerprinting for localization.…”

“…The research [61], however, is limited due to many factors. First of all, it involves taking images at each step, which utilizes a substantial amount of phone battery.…”