论文标题
微型航空车的WiFi惯性室内姿势估计
WiFi-Inertial Indoor Pose Estimation for Micro Aerial Vehicles
论文作者
论文摘要
本文提出了一个带有单个WiFi接入点的微型航空车（MAV）的室内姿势估计系统。基于计算机视觉的常规方法受照明条件和环境纹理的限制。我们的系统没有视觉限制，可以立即部署，在没有任何部署成本的情况下为现有的WiFi基础架构工作。我们的系统由两个耦合模块组成。首先，我们提出了一个排序角度（AOA）估计算法，以估计MAV态度并解开AOA的定位。其次，我们制定了一个WiFi惯性传感器融合模型，该模型融合了AOA和通过惯性传感器测量的探光度，以优化MAV姿势。考虑到MAV的实用性，我们的系统被设计为在未知环境中需要敏捷飞行的实时和初始化。室内实验表明，我们的系统达到了姿势估计的准确性，位置误差为$ 61.7 $ cm，态度误差为$ 0.92^\ circ $。
This paper presents an indoor pose estimation system for micro aerial vehicles (MAVs) with a single WiFi 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 WiFi 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 WiFi-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 $0.92^\circ$.
