Multi-sensor fusion positioning and navigation system based on Wi-Fi CSI

Abstract

Indoor localization is essential for navigation, tracking, and path planning applications. Traditional systems, based on sensors like inertial devices and LiDAR, offer high accuracy but are costly and prone to cumulative errors. We propose a cost-effective multi-sensor fusion system specifically tailored for two-dimensional localization of two-wheeled mobile robots, combining Wi-Fi channel state information (CSI) and odometry, using an extended Kalman filter (EKF) and an adaptive Monte Carlo localization (CSI-AMCL) algorithm to enhance accuracy. Our innovative 1D convolutional neural network (1D-CNN) based on residual networks effectively processes CSI data, improving adaptability in complex environments by addressing the vanishing gradient issue. Our approach increases accuracy by 56% compared to Wi-Fi fingerprinting. Tests show a 20.1% improvement over WIO-EKF and a 36.3% improvement over Fusion-dhl. This demonstrates the potential of our method for enhancing multi-sensor fusion systems.