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°.

中文翻译：

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微型飞行器 Wi-Fi 惯性室内姿态估计


本文介绍了一种用于具有单个 Wi-Fi 接入点的微型飞行器 (MAV) 的室内姿态估计系统。基于计算机视觉的传统方法受到照明条件和环境纹理的限制。我们的系统不受视觉限制，可立即部署，可在现有 Wi-Fi 基础设施上运行，无需任何部署成本。我们的系统由两个耦合的模块组成。首先，我们提出了一种到达角 (AoA) 估计算法来估计 MAV 姿态并解开 AoA 进行定位。其次，我们制定了 Wi-Fi-惯性传感器融合模型，融合了迎角和惯性传感器测量的里程计，以优化 MAV 姿态。考虑到MAV的实用性，我们的系统被设计成实时且免初始化的，以满足未知环境下敏捷飞行的需要。室内实验表明，我们的系统实现了位姿估计的精度，位置误差为61.7 cm，姿态误差为0.92°。