In this paper, a novel method for autonomous navigation for an extra-terrestrial body landing mission is proposed. Based on state-of-the-art crater detection and matching algorithms, a crater edge-based navigation method is formulated, in which solar illumination direction is adopted as a complementary optical cue to aid crater edge-based navigation when only one crater is available. To improve the pose estimation accuracy, a distributed Extended Kalman Filter (EKF) is developed to encapsulate the crater edge-based estimation approach. Finally, the effectiveness of proposed approach is validated by Monte Carlo simulations using a specifically designed planetary landing simulation toolbox.

To satisfy the needs of precise pin-point landing missions in deep space exploration, this paper proposes a method based on feature line extraction and matching to estimate the attitude and position of a lander during the descent phase. Linear equations for a lander's motion parameters are given by using at least three feature lines on the planetary surface and their two-dimensional projections. Then, by taking advantage of Singular Value Decomposition (SVD), candidate solutions are obtained. Lastly, the unique lander's attitude and position relative to the landing site are selected from the candidate solutions. Simulation results show that the proposed algorithm is able to estimate a lander's attitude and position robustly and quickly. Without an extended Kalman filter, the average errors of attitude are less than 1° and the average errors of position are less than 10 m at an altitude of 2,000 m. With an extended Kalman filter, attitude errors are within 0·5° and position errors are within 1 m at an altitude of 247·9 m.