Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles
Shengkai Zhang, Wei Wang, Tao Jiang
Abstract
This article presents an indoor pose estimation system for microaerial vehicles (MAVs) with a single Wi-Fi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing Wi-Fi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a Wi-Fi-inertial sensor fusion model that fuses the AoA and the odometry measured by inertial sensors to optimize MAV poses. Considering the practicality of MAVs, our system is designed to be real-time and initialization-free for the need of agile flight in unknown environments. The indoor experiments show that our system achieves the accuracy of pose estimation with the position error of 61.7 cm and the attitude error of 0.92°.