Abstract
Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning.
Highlights
Location has become increasingly important for many position-based services (PbS)
Many indoor positioning methods rely on pre-installed infrastructures and can provide reasonable accuracy, such as UWB ranging anchor-based [1,2], Wi-Fi access points (APs)-based [3,4], Ultrasound [5,6]-based and so on
Graph-based optimization is currently widely adopted in robotics for solving the simultaneously localization and mapping (SLAM) problem [20]
Summary
Location has become increasingly important for many position-based services (PbS). the global navigation satellite system (GNSS) can satisfy the needs for most outdoor situations, indoor positioning still remains a challenge. Many indoor positioning methods rely on pre-installed infrastructures and can provide reasonable accuracy, such as UWB ranging anchor-based [1,2], Wi-Fi access points (APs)-based [3,4], Ultrasound [5,6]-based and so on Among these methods, Bluetooth low-energy (BLE) beacons have great potential due to their advantages: Sensors 2018, 18, 3736; doi:10.3390/s18113736 www.mdpi.com/journal/sensors. Range-based methods adopt a pre-defined radio frequency (RF) path-loss model to estimate the distance between the receivers (users) and the beacons. The proposed method in the paper provides an efficient way for estimating the pre-requisites for BLE-based positioning. The proposed method can estimate both the beacon positions and the RFM using graph-based optimization. The RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model).
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