Horizontal velocity estimation via downward looking descent images for lunar landing

In lunar landing missions, it is very important to closely estimate the horizontal velocity of a descending spacecraft for achieving a successful and safe lunar landing. The purpose of this paper is to present a novel, vision-aided approach for the accurate, efficient, and robust estimation of such horizontal velocity. Our algorithm processes images from a downward-looking camera, as well as attitude and altitude information from other sensors, to estimate horizontal velocities. During descent, images vary greatly in scale, orientation, and viewpoint. To begin, the scale-invariant feature transform (SIFT) algorithm copes with such shifts, so one is able to use extracted keypoints to establish correspondences between consecutive descent images. Then, matched SIFT keypoints are projected to the level ground plane according to the measurement of the camera state and the central projection imaging collinear equation. A 1-point random sample consensus (RANSAC) algorithm is adopted to remove mismatched keypoints. From each correctly matched keypoint pair, the algorithm derives a hypothesis for the spacecraft displacement relative to lunar ground, since those keypoints represent measurements of the same position on the lunar surface. From the bundle of displacement hypotheses, our algorithm robustly recovers the mode of the sample distribution. This final horizontal displacement estimate of the spacecraft is obtained by using the mean shift method to search for an appropriate answer among these hypotheses. In combination with the time interval between shots, the horizontal velocity is estimated. Additionally, a digital signal processor with field-programmable gate array architecture is also presented to implement velocity estimation in real time. We evaluate the performance of our algorithm based on numerous simulated image sequences and real flight images compared with the descent image motion estimation system approach and an extended Kalman filter monocular simultaneous localization and mapping method.