Homography-based depth recovery with descent images

In planetary landing exploration task, the images captured by the landing camera are nearly along optical axis which results in multi-resolution images of same terrain surface. Recovering the surface shape of landing terrain from descent imagery is of great value for lander to choose safe landing area. In this paper, a homography-based depth recovery method with descent images is addressed. At first, the parallax and scale change in descent images are analyzed. Second, the camera motion is optimized with SIFT features correspondence constraints. For dense depth recovery, a set of virtual parallel planes is assumed to slice the terrain and each plane induces a homography to warp back the second image to first image plane. Zero-normalized cross-correlation score is chosen to compute the correlation score and the correlation curve is smoothed by two Gaussian filters. The depth for each pixel is determined by the plane which has highest correlation value. At the end, some experiments are conducted, including different correlation computation, depth recovery with different terrain, and the error tests. The results show that the discussed method is feasible to recover the depth information overall.