Autonomous Navigation Method for Planetary Landing Based on Observability Analysis of Sequential Images
Jiaxing Li, Dayi Wang, Runran Deng, Tianshu Dong, Bowen Hou · Guidance Navigation and Control · 2024
Sequential images provided by optical cameras are ideal for realizing high-precision autonomous navigation for planetary landings, but due to limited computational resources, a large amount of image information cannot be processed on deep space probes, thus the rich landmark features in the sequential images must be selected. The contribution of landmark features to navigation accuracy is usually measured in terms of observability degrees, but the traditional method only observes the locally optimal landmarks at a single moment, and the navigation accuracy will be affected by the gradual decrease of observability degrees when observing the same landmark several times in a row before landmarks are reoptimized. In this paper, we first establish of autonomous navigation model of planetary landing segment based on sequential images. Then we construct a sequential image observability degree for multiple observations, proving that it is a convex function, and giving the interval of the minimum value point, which guides the selection of landmarks with the highest observability for multiple moments. Finally, numerical simulations were conducted and verified that using sequential image observability degree to optimize landmarks for navigation can achieve higher navigation accuracy than traditional single moment selection methods, and it can provide theoretical support for the sequential image autonomous navigation in the landing segment of deep space exploration.