Adversarial Learning-Enabled Automatic WiFi Indoor Radio Map Construction and Adaptation With Mobile Robot

Abstract

Location-based service (LBS) has become an indispensable part of our daily lives. Realizing accurate LBS in indoor environments is still a challenging task. WiFi fingerprinting-based indoor positioning system (IPS) has achieved encouraging results recently, but the time and labor overhead of constructing a dense WiFi radio map remains the key bottleneck that hinders it for real-world large-scale implementation. In this article, we propose WiGAN an automatic fine-grained indoor ratio map construction and the adaptation scheme empowered by the Gaussian process regression conditioned least-squares generative adversarial networks (GPR-GANs) with a mobile robot. First, we develop a mobile robotic platform that constructs the spatial map and radio map simultaneously in the easily accessed free space. GPR-GAN first establishes a Gaussian process regression (GPR) model using the real received signal strength (RSS) measurements collected by our robotic platform via LiDAR SLAM in the free space. Then, the outputs of the GPR are adopted as the input of GAN's generator. The learning objective of GAN is to synthesize realistic RSS data in a constrained space where it has not been covered and model the irregular RSS distributions in complex indoor environments. Real-world experiments were conducted in a real-world indoor environment, which confirms the feasibility, high accuracy, and superiority of WiGAN over existing solutions in terms of both RSS estimation accuracy and localization accuracy.