Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization

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.

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

“…The average localization error for the eight participants was 1.05 ± 0.44 m. This result is better than those obtained in the related literature [ 14 , 15 , 16 , 17 , 18 ]. However, a direct comparison between the studies is not possible because all the related works were evaluated in different scenarios of different shapes and sizes and using different transmitting/receiving devices.…”

“…That is why in this work, it was chosen to put a ‘stationary’ beacon in each room of the apartment and active or mobile beacons on objects with which a person usually interacts. One way to reduce the average position error obtained is to increase the number of beacons, as verified in Zuo et al [ 18 ], thereby increasing the number of beacons from 24 to 48, they managed to reduce the position error from 4.69 m to 3.25 m. However, the more beacons used, the more expensive the IPS. The trade-off between cost and precision must be found.…”

“…A previous study deployed 48 beacons uniformly in an indoor environment [ 18 ]. They demonstrated that the greater the number of beacons deployed in the environment, the smaller the error of their location.…”

“…A Graph-based optimization method that solves the SLAM problem is proposed in Zuo et al [ 18 ]. It combines PDR and fingerprinting.…”

Robot Localization in Water Pipes Using Acoustic Signals and Pose Graph Optimization

“…Pose-graph optimization has been applied to localization of robots in pipes using vision [ 7 ], a fusion of IMU and tether cable information [ 17 ], and periodic radio wave amplitude [ 41 ]. Recent work in the use of acoustic echoes for localization in pipes [ 42 ] could be applied to pose-graph optimization; these acoustic measurements are range-only measurements of features, which have been used in pose-graph optimization in other applications [ 43 , 44 ]. As applied in many of these methods, the use of an approach based on the Levenberg–Marquardt method, such as general graph optimization [ 45 ], may be sufficient to find an optimal trajectory estimate given the dynamic information presented in this paper.…”