Fast autonomous crater detection by image analysis-for unmanned landing on unknown terrain

Abstract

Unmanned landing on unknown terrain such as planetary surfaces requires the in-situ estimation of surface irregularities like craters, ridges and other deformities. Moreover, to facilitate safe landing, the surface estimation has to be done in as little time as possible. In this paper, we present an algorithm to address the above two issues in the context of crater presence on the terrain. Detection of craters is done on images of the probable landing surfaces and the computation time required for the detection is subsequently reduced in the proposed method using image analysis approaches like standard deviation filtering, morphological operations and validation of crater presence by texture extraction. We have achieved a 85-89% true positive (TP) rate on large craters and 79-82% TP rate on small craters. We have conducted our experiments on real images of Mars and the Moon, collected by space-crafts named 2001 Mars Odyssey and the Lunar Reconnaissance Orbiter, respectively. Empirical evidences indicate that the proposed method achieves a commendable TP rate and a subsequent improvement in the time required for detection as compared to existing methodologies.