Derived Feature-Based Estimation Method for Autonomous Navigation of Extraterrestrial Landing

During the landing of extraterrestrial bodies, landmark features such as craters on the celestial body's surface are important observational targets for autonomous optical navigation. They provide absolute navigation information for the estimation of the lander's state. However, such features may not be abundant in the neighborhood of a flat landing area. Besides, as the lander descends, the camera's field of view shrinks, and the features that were once observable would gradually go out of the field of view. These conditions would lead to the issue of sparse features and hinder the autonomous optical navigation performance of the lander. To solve the problem of sparse features, this paper proposes the concept of “feature derivation” to enrich the navigation information. On this basis, a derived feature-based estimation method is developed. Two feature derivation models are established by using the sequence images captured by the camera and the spatio-temporal correlations of the features respectively. Based on the error analysis of the derivation models, the derived results are fused to improve the feature derivation accuracy. The angles between the derived and actual features are computed and adopted for decoupled estimation of the lander's position and attitude. Finally, a set of numerical simulations are devised to demonstrate the performance of the proposed method and verify the feasibility of using the derived features for state estimation.