# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 6 Conclusions  
To improve localization ability for large-scale indoor spaces, this paper proposed the 3D-WBQM framework, which mainly consists of four main sections:  
1. MEMS sensor-based inertial odometry, which contains the multi-level observations and trajectory optimization modules;
2. MLP-based crowdsourced trajectories evaluation strategy, which can autonomously predict the error of each collected trajectory and improve the robustness of the final generated navigation database;
3. Three different kinds of landmark points and the DTW-based universal landmark-detection algorithm, which provide accurate absolute locations for the MEMS sensor-based approach; and
4. Multi-source fusion-based localization using RUKF.  
A universal model was proposed that adaptively integrates the different location sources and adjusts the weight of each location source in real time.  
The experimental results demonstrated that the presented 3D-WBQM framework is capable of achieving autonomous and highly adaptive 3D indoor-positioning accuracy for large-scale indoor areas, with a final evaluated positioning error lower than 1.66 m in 75% in complex and multi-floor contained indoor environments, and a final meter-level positioning accuracy in indoor areas supported by Wi-Fi FTM.  
Figure 17: Comparison result of the 3D-WBQM framework and the WFS-F.  
The advantages of the proposed 3D-WBQM framework are that daily-life trajectories are autonomously evaluated and integrated for crowdsourced Wi-Fi fingerprinting database generation using three different kinds of landmarks. In addition, the proposed multi-level-observation-assisted inertial odometry can maintain high precision after long-term use under magnetic interference in a contained large-scale indoor environment.  
The proposed 3D-WBQM framework also has some disadvantages. First, the accuracy of crowdsourced trajectories is limited by the navigation time and by complex and changeable indoor scenes, and the positioning errors grow with time. Second, the performance of MLP-based positioning error evaluations can be further improved by considering and extracting more human motion and handheld-related features for a more intelligent fusion and construction of a crowdsourced navigation database.  
In the future, with the development of the Internet of Things and artificial intelligence, mobile terminals will support more of the emerging positioning and perception technologies such as light fidelity, light detection and ranging (Lidar), and RIS-assisted mmWave networks, which will provide a more robust and accurate performance of indoor positioning, mapping, perception, and communication.  
This paper is a collaborative work by all the authors. [PERSON] proposed the idea and implemented the system. [PERSON] performed the experiments, analyzed the data, and wrote the manuscript. [PERSON] and [PERSON] aided in proposing the idea, provided suggestions, and revised the rough draft. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The Hong Kong Polytechnic University (1-ZVN6, 4-BCF7); The State Bureau of Surveying and Mapping, P.R. China (1-ZVES); and the Hong Kong Research Grants Council (T22-505/19-N).  
Data sharing is not applicable to this article.  
The authors declare that they have no conflict of interest to disclose.