Multi-type feature fusion visual navigation for asteroid landing

In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features, such as significant recognition and extraction errors, low computational efficiency, and limited navigation accuracy, a novel approach for multi-type fusion visual navigation is proposed. This method aims to overcome the limitations of single-type features and enhance navigation accuracy. Analytical criteria for selecting multi-type features are introduced, which simultaneously improve computational efficiency and system navigation accuracy. Concerning pose estimation, both absolute and relative pose estimation methods based on multi-type feature fusion are proposed, and multi-type feature normalization is established, which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation. The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.