A Dual-Mode Approach for Vision-Based Navigation in a Lunar Landing Scenario

Luca Ostrogovich, Roberto Del Prete, Giuseppe Tomasicchio, Nicolas Longepe, Alfredo Renga; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6799-6808

In this research a novel approach for autonomous spacecraft navigation particularly in lunar contexts is presented focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position with an average accuracy of approximately 270 m for the absolute navigation mode while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems--akin to those onboard spacecraft--were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.