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| Abstract: |
| Autonomous landing has become a core technology of unmanned aerial vehicle (UAV) guidance, navigation, and control system in recent years. This paper discusses the vision-based relative position and attitude estimation between fixed-wing UAV and runway, which is a key issue in autonomous landing. Images taken by a airborne camera was used and a runway detection method based on long-line feature and gradient projection is proposed, which solved the problem that the traditional Hough transform requires much calculation time and easily detects end points by mistake. Under the premise of the known width and length of the runway, position and attitude estimation algorithm used the image processing results and adopted an estimation algorithm based on orthogonal iteration. The method took the objective space error as error function and effectively improved the accuracy of linear algorithm through iteration. The experimental results verified the effectiveness of the proposed algorithms. |
| Key words: autonomous landing visual navigation Region of Interest (ROI) edge detection orthogonal iteration |
| DOI：10.11916/j.issn.1005-9113.20064 |
| Clc Number:V249.31; TP391.7 |
| Fund: |
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| Descriptions in Chinese: |
| 基于单目视觉的固定翼无人机自主着陆定位研究 许彧文，曹云峰，张洲宇 （南京航空航天大学 航天学院， 南京 210000） 中文说明：近年来，无人机自主着陆技术已成为无人机导航、制导与控制系统的核心技术。为解决自主着陆过程中固定翼无人机与跑道之间基于视觉的相对位置和姿态估计问题，本文利用机载相机拍摄的序列图像，通过边缘检测的方式获取跑道角点信息，进一步基于正交迭代算法确定六自由度的位姿参数：首先基于频域残差对图像进行显著性分析，通过连通域标记和候选框筛选估计跑道所在的感兴趣区域；然后在感兴趣区域中提取跑道的边缘直线，由于着陆末端跑道纵向边缘具有明显的长度和相位特征，在利用霍夫变换定位纵向边缘的基础上，结合跑道平面梯度信息估计横向边缘位置，解决了传统的霍夫变换计算耗时，且难以检测端点的问题；最后将角点图像坐标作为输入，通过线性算法获取旋转矩阵初值，在此基础上以目标空间误差为误差函数，通过正交迭代优化参数估计结果，图像点坐标误差较小时，该方法相比线性算法精度明显提升，且具有良好的实时性。 关键词：自主着陆，视觉导航，感兴趣区域，边缘检测，正交迭代 |
