Visual navigation based on curve matching for planetary landing in unknown environments

For planetary landing missions, accurate surface relative state knowledge to safely land near science targets is required. However, the lack of on-board map poses a technical challenge in the process of implementing such a navigation system. Moreover, traditional feature point observations are not sufficient for guaranteeing robust performance of the algorithm under challenging environmental conditions like texture-less terrain. This paper presents a novel visual navigation scheme based on curve matching for planetary landing in unknown environments. An analytical measurement model, which contains the lander's current and previous states, is derived by using curve matches in descent images for the first time. The derived measurement model and the state information sensed by inertial measurement unit are integrated in unscented Kalman filter algorithm. Also, the observability of the proposed navigation system is analyzed. In particularly, the Fisher matrix is used to evaluate the navigation performance. It is shown that the superior accuracy of the proposed method is obtained compared to point-based approaches. Simulation results demonstrate the effectiveness and high accuracy of the proposed navigation method.