针对小型无人机在无卫星导航信号条件下的导航问题, 结合光流及地标定位设计了使用摄像头、惯性测量器件、超声测距仪等传感器融合的无人机室内导航方法. 文章使用补偿角速率的光流微分法计算帧间像素点小位移, 并用前后误差算法提取精度较高的点, 避免像素点跟踪错误, 提高了光流测速的精度 对得到的光流场用均值漂移算法进行寻优, 得到光流场直方图峰值, 以此计算光流速度. 本文提出了无累积误差的连续地标定位算法, 实时测量无人机位置. 通过多速率卡尔曼滤波器对观测周期不一致的位置、速度信息进行最优估计. 在搭建的八旋翼无人机平台上试验, 将位置与速度测量结果分别与激光和PX4FLOW数据对比, 结果表明该导航方法可以有效抑制定位跳变与光流测量噪声误差, 给出精确的位置与速度估计.

-Problems for the Navigation satellite navigation signals in the absence of conditions, and in conjunction with optical flow landmark UAV design indoor navigation positioning method using the camera, inertial measurement device, an ultrasonic range finder sensor fusion article using the compensation angle computing optical flow rate of the inter-pixel differentiation small displacements, and extracted with high precision before and after the point of error algorithm, to avoid the tracking error pixel, improve the accuracy of optical flow speed　optical flow field obtained for the mean shift algorithm optimization, the histogram peak optical flow, in order to calculate the optical flow velocity. in this paper, the landmark location algorithm continuously accumulated error is measured in real time the position of the UAV by multirate Kalman filter observation period inconsistent positions , optimal estimation speed information. tested on eight rotor UAV platform structures, the position a