A Dual Quaternion Based Visual Odometry Method for a Lunar Lander

Visual sensors have a major advantage over traditional sensing methods for navigation in unknown environments since they provide rich information about the environment. However, they do not offer direct navigation measurements, and the images must be pre-processed. To extract meaningful information, the development of reliable and computationally efficient algorithms are required. This study presents a visual odometry solution that can effectively operate to estimate the pose of a small lander for the soft moon landing. For this purpose, an algorithm is designed to estimate the angular and translational velocities of the spacecraft during the descent and landing phases. A Kalman filter-based algorithm is proposed, which uses the dual quaternion formulation to provide a linear Kalman filter for velocity estimation. The proposed algorithm is tested in a simulation environment for a lunar landing scenario.