Indoor positioning of shoppers using a network of Bluetooth Low Energy beacons

Abstract

Abstract-In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our nodegraph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a "nearest-beacon" approach as a base-line; exponentially averaged weighted range estimates; and a particlefilter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilities.

Global map matching using BLE beacons for indoor route and stay estimation

“…In addition, research on location estimation using BLE beacons also include the proximity method [15], particle filtering method [6,23], trilateration [2], and fingerprint method [8,13]. The proximity method is a simple method that involves setting a position where the beacon with the highest RSSI is the estimated position when radio waves from the beacons are received.…”

“…Davidson et al [5] stated that there are studies such as map-matching using wall constraints and topological map-matching for indoor navigation. For example, Dickinson et al [6] proposed a location estimation method using a node graph and particle filter. The search space can be restricted by using the node graph, and the accuracy of location estimation is improved.…”

Visual Map Construction Using RGB-D Sensors for Image-Based Localization in Indoor Environments

“…. , −1 }; (8) if mat ≥ mat and dep ≤ dep (9) V * ←Perform the g2o algorithm with the camera pose V; (10) break; (11) end ( query image captured by the smartphone's camera. This part will focus on the online phase, which is the image-based localization.…”

“…However, the localization system based on Wi-Fi signals usually cannot provide users with stable results because moving objects or persons affect the signal strength. In addition, Bluetooth beacons are also used for indoor localization [6][7][8]. The effective range of Bluetooth signals is approximately 5-10 meters, which leads to frequent beacon switching and results in high power consumption.…”

Localizable Mobile Payment Shopping Cart based on Location Fingerprint Algorithm

“…Ultrasonic and infrared positioning system [3] is greatly affected by the environment and is not suitable for supermarkets with complex environment. Bluetooth location [4] is a short-distance and low-power wireless transmission technology, which is mainly used in small range location, single-floor hall or warehouse environment.…”