摘要
无人机序列影像具有高分辨率、高重叠度的特点,被用于大范围场景三维重建。现有多视图三维重建算法在处理大规模无人机序列影像时耗时严重、结果不稳定。将无人机低精度GPS/IMU参数融合到大规模无人机序列影像三维重建过程中,设计实现了无人机序列影像批处理三维重建方法。利用低精度GPS/IMU先验信息进行图像匹配,降低了重建中图像匹配的时间消耗以及减少误匹配;通过建立极线图和绘制多视图中点的轨迹,并结合低精度GPS/IMU信息,一次性求解全局坐标系下所有图像的旋转矩阵,只执行一次捆绑调整函数,降低了重建时优化的时间复杂度。通过实验验证了本方法在保证精度的同时提高了效率。
The images sequence captured by UAV has the high resolution and high overlapping characteristics, so it is used in a wide range of scene reconstruction. However, the result of these state-of-art multi-view 3D reconstruction algorithms is unstable when dealing with large-scale UAV thousands of images. In this regard, the UAV low accuracy GPS / IMU parameters sequence was fused to the large-scale UAV image reconstruction process and the UAV sequence batched image reconstruction method was proposed. The image matching time and false matching number was reduced by using low-precision GPS / IMU priori information. Through the establishment of a polar graph and drawing the midpoint of the track multi-view, combined with low-accuracy GPS / IMU information, the global coordinate system rotation matrix of all the images was disposably solved, executing only when a bundle adjustment function, reducing the reconstruction optimization of time complexity. Experimental verification of this method ensures accuracy while improving efficiency.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2015年第10期2409-2415 2421,2421,共8页
Journal of System Simulation
基金
国家自然科学基金(41401465
41371384)
