An Indoor 3-D Quadrotor Localization Algorithm Based on WiFi RTT and MEMS Sensors

Abstract

With the development of quadrotor-based location services, accurate indoor quadrotor localization plays an important role in various applications. Tight fusion refers to the process of integrating multisensor data into state estimation for optimization, and finally obtaining pose information. In this article, we propose a novel tight fusion method for quadrotor localization by fusing the WiFi round-trip time (RTT) and built-in smartphone microelectromechanical sensors. Unlike existing 3-D localization frameworks, the key contribution of the proposed method is to integrate 3-D outlier detection, state estimation, coordinate frame alignment, and data fusion into a nonlinear filtering framework. Specifically, this method is divided into four main steps: 1) the coordinates of the mobile phone and the quadrotor are converted to the same coordinate system through the coordinate alignment method we propose; 2) the proposed outlier detection method is used to obtain the 3-D coordinates of the quadrotor based on WiFi RTT; 3) the WiFi RTT localization results are integrated into an error-state Kalman filter (ESKF) to perform the integrated localization of the quadrotors; and 4) a Rauch–Tung–Striebel (RTS) smoothing method is used to optimize the localization results. The experimental results demonstrate that the proposed method outperforms the classic localization method in terms of both accuracy and robustness.