Crater Detection Method using Principal Component Analysis and its Evaluation

Abstract

In this study, a crater detection method for a moon-landing system with low computational resources is proposed. The proposed method is applied to the Smart Lander for Investigating Moon (SLIM), which aims for a pinpoint landing on the moon. According to this plan, surface images of the moon will be captured by a camera mounted on the space probe, and the craters are to be detected from the images. Based on the positional relationship between detected craters, the method estimates the exact flight position of the space probe. Because the computational resources of SLIM are limited, rapid and accurate crater detection must be performed using fixed-point arithmetic on a field-programmable gate array (FPGA). This study proposes a crater detection method that uses principal component analysis (PCA). The computational processing for crater detection by PCA is performed by product-sum operations, which are suitable for fixed-point arithmetic. Moreover, this method is capable of parallel processing; hence high-speed processing is expected. This study not only introduces a crater detection method using PCA but also evaluates the properties of this method.

Thermal Cycle Tests of CLCC Solder Joints: Influence of Substrate, Solder, and Pad Patterns

“…At first, crater extraction from lunar surface images by the SLIM-CAM is executed. 4) Subsequently, the current spacecraft location is searched by matching the extracted craters with the crater database prepared before launch. 5),6) When SLIM carries out the vision-based navigation, the orbital velocity of SLIM is as high as maximum 1 to 2 km/s.…”