A 3d Map Aided Deep Learning Based Indoor Localization System for Smart Devices

Abstract

Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

Indoor Positioning for Smart Devices Based on Sensor Fusion With Particle Filter: Localization and Map Updating

“…The data gathering and map creating methods are similar to the previous work (Yang, et al, 2020), in which the 3D map is built by the RGBD SLAM (RTAB-map) with Kinect V1 RGBD camera and LooMo robot, and Wi-Fi fingerprints in radio map are collected by a laptop with a 3 m interval, as a balance between labor cost and accuracy, between every two calibration points. In this work, we implement experiments in another office hallway (55 m*3 m) at the Ohio State University, see Figure 3 and Table 1.…”

“…Test datasets are built on 18 points, where 3 laptops (VAIO Z Canvas, ThinkPad X1, HP ProBook) and 2 cameras (Kinect V1, SONY XPERIA X smartphone) are used for collecting Wi-Fi RSS and query images respectively. The configuration of test points is set as in our previous work (Figure 6) (Yang et al, 2020), which means that black points are randomly set in the hallway and green points are set between calibration points aligned with the middle line of the hallway where the mapping robot LooMo will pass over it. Therefore, the query images recorded by Kinect have 6DoF camera pose obtained from RGBD SLAM as ground-truth, while query images recorded by the smartphone have only 2D location ground-truth.…”

“…In the previous work (Yang et al, 2020), we presented a system that allows user to gain static 6DoF (map frame) indoor positioning information with meter level average accuracy, by firstly running a Bayes'-based WF algorithm to generate a coarse location estimation (Figure 1). The fingerprinting matching problem in WF system is solved to obtain the posterior distribution by using Bayes' rule, which is described as,…”