Overview of Terrain Relative Navigation Approaches for Precise Lunar Landing

TLDR

Traditional inertial‑based lunar landings lack the precision needed for a 100 m autonomous landing, but Terrain Relative Navigation (TRN) can reduce the error to meet this requirement through global, local, and velocity estimation functions. The project seeks autonomous lunar landings within 100 m of a target, using TRN to augment inertial navigation with landmark‑based position or bearing data. TRN functions are realized with active range sensing or passive imaging, and the paper surveys many methods, presenting high‑fidelity simulations of contour matching and area correlation with active sensors. The study concludes that TRN depends on an a‑priori reference map and outlines existing and forthcoming lunar imaging and digital elevation datasets that can support it.

Abstract

The driving precision landing requirement for the Autonomous Landing and Hazard Avoidance Technology project is to autonomously land within 100 m of a predetermined location on the lunar surface. Traditional lunar landing approaches based on inertial sensing do not have the navigational precision to meet this requirement. The purpose of Terrain Relative Navigation (TRN) is to augment inertial navigation by providing position or bearing measurements relative to known surface landmarks. From these measurements, the navigational precision can be reduced to a level that meets the 100 m requirement. There are three different TRN functions: global position estimation, local position estimation and velocity estimation. These functions can be achieved with active range sensing or passive imaging. This paper gives a survey of many TRN approaches and then presents some high fidelity simulation results for contour matching and area correlation approaches to TRN using active sensors. Since TRN requires an a-priori reference map, the paper concludes by describing past and future lunar imaging and digital elevation map data sets available for this purpose.