Accurate crater detection based on dark–bright boundary identification for terrain relative navigation

Crater-based terrain relative navigation (TRN) can be used for precise pinpoint landing in lunar landing missions. However, the existing crater detection algorithms (CDAs) cannot accurately extract the parameters of the crater. To address these issues, this paper presents an accurate crater detection method that is based on dark-bright boundary identification. First, a correlation template is generated from selecting sample images for craters of interest. Then, a dark-bright boundary identification method based on local correlation max-min matching is proposed to obtain the boundary of the crater. Finally, the diameter and position of craters are determined by circle fitting from boundary points. With images from the Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) and Narrow Angle Camera (NAC) in different scenes, the experimental results demonstrate that the proposed algorithm reduces the position error by 27.45% and the diameter error by 53.02% compared with the PCA-based algorithm. Thus, the presented method can detect craters parameter accurately from navigation images for TRN.