基于FastSLAM算法的无人机着陆方法  

Research on UAV Landing Based on FastSLAM Algorithm

作　　者：张烨林[1] 王养柱[1] 韩震[1] 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191

出　　处：《电光与控制》2017年第9期83-87,108,共6页Electronics Optics & Control

摘　　要：为解决无人机在GPS信号失效下的着陆导航问题,采用基于FastSLAM算法的导航方法。首先,基于机载多普勒雷达测量的地速,用含有高斯噪声的非线性方程来描述无人机着陆的运动模型;搭载在无人机上的激光雷达测量相对环境路标的位置和方位角用于构建系统的观测模型。基于无人机着陆段的系统模型,采用粒子滤波估计无人机的路径,扩展卡尔曼滤波估计环境路标位置。获得无人机的路径估计后,采用反正切形式的跟踪微分器获得速度估计。仿真验证了在选取适当的粒子数和环境路标情况下,FastSLAM算法满足无人机着陆定位精度和实时性要求,是一种可行的着陆段导航方法。In order to solve the problem of UAV＇s autonomous landing when GPS signal is failed, a UAV landing method based on FastSLAM algorithm is adopted. First, based on the ground speed measured by Doppler radar, the motion model is described by nonlinear equation with Gaussian noise. The UAV uses its laser radar to obtain the distance and azimuth relative to environmental landmarks for constructing the system observation model. Path estimation and environmental landmark estimation are implemented by particle filter and extended Kalman filter respectively. The velocity estimation is obtained by using tracking differentiator of the arctangent form. Simulation results show that by choosing appropriate number of particles and environmental landmarks, FastSLAM algorithm can meet the requirement on landing precision and real-time performance, and is an effective landing navigation method.

关 键 词：无人机着陆 FAST SLAM 粒子滤波 扩展卡尔曼滤波 

分 类 号：V249.3[航空宇航科学与技术—飞行器设计]