Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse...Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse projection ray approach to address the relative position and attitude estimation by using feature points and monocular vision. It consists of two stages： absolute orienta- tion and depth recovery. In the first stage, Umeyama＇s algorithm is used to fit the three-dimensional （3D） model set and estimate the 3D point set while in the second stage, the depths of the observed feature points are estimated. This procedure is repeated until the result converges. Moreover, the effectiveness and convergence of the proposed algorithm are verified through theoreti- cal analysis and mathematical simulation.展开更多
森林中立木的单木定位是林业遥感领域中的重要问题,基于单目相机的视觉即时定位与建图(simultaneous localization and mapping,SLAM)算法是室外空间定位和建图的重要手段,解决了由于树木冠层遮挡导致的全球导航卫星系统信号缺失问题,...森林中立木的单木定位是林业遥感领域中的重要问题,基于单目相机的视觉即时定位与建图(simultaneous localization and mapping,SLAM)算法是室外空间定位和建图的重要手段,解决了由于树木冠层遮挡导致的全球导航卫星系统信号缺失问题,然而现有的单目视觉SLAM算法无法实现样地内立木直接定位。为解决此问题,基于单目视觉SLAM算法,提出了单木SLAM(individual tree SLAM,Indi-tree SLAM)算法。该算法通过使用图像序列进行相机位姿估计、地图尺度恢复、单木位置判断和单木位置坐标计算等过程可实现样地中的立木直接定位。采用相机对3块边长为40 m的方形样地进行样地扫描,对Indi-tree SLAM算法进行精度验证。实验结果表明,Indi-tree SLAM算法所计算的样地立木坐标在沿x轴和y轴方向的均方根误差均为0.44 m,平均定位误差为6.3%。Indi-tree SLAM算法实现了样地内立木的直接定位,缩短了森林结构参数测量时间,为森林资源调查提供了一种准确、高效的可行性方案。展开更多
基金Program for Changjiang Scholars and Innovative Research Team in University (IRT0520)Ph.D.Programs Foundation of Ministry of Education of China (20070213055)
文摘Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse projection ray approach to address the relative position and attitude estimation by using feature points and monocular vision. It consists of two stages： absolute orienta- tion and depth recovery. In the first stage, Umeyama＇s algorithm is used to fit the three-dimensional （3D） model set and estimate the 3D point set while in the second stage, the depths of the observed feature points are estimated. This procedure is repeated until the result converges. Moreover, the effectiveness and convergence of the proposed algorithm are verified through theoreti- cal analysis and mathematical simulation.
