Vision-Based Navigation For Lunar Probe Softlanding

Autonomous navigation is one of the core technologies of the second and third phase of China Lunar Exploration Project, could determine the success of pin-point soft landing. With the development of three decades, driven by the sensor technology and significant improvement in chip's computing power, visual navigation methods has got the advantages of low cost, low power and high reliability, which has been widely used in the navigation systems of ground robots, unmanned aerial vehicles and underwater autonomous vehicles. With the vision processing algorithms become more sophisticated, the visual navigation system used in lunar soft landing missions, can greatly improve the navigation accuracy of explorer's soft pin-point landing. Thetelated problems of visual navigation and simulation methods will be researched. For the simulation of visual navigation and hazard detection in lunar soft landing, a modeling of terrain surface is proposed. The high-resolution digital elevation map will be generated from low-resolution data based on a square-square subdivision fractal function. And the lunar impact craters will be added to the model according to the latest database and the statistic results of observation. Some of the modeling results will be presented.Based on the analysis of soft landing visual navigation mission requirement, an improved SIFT feature detecting and matching algorithm is proposed. The samples are chosen in the "blob" region of Gaussian difference images. The extremum is obtained in limited times of iterative calculations. Then the relative descriptor is calculated. The parameter settings are established with large amount of features extraction and matching experiments on different types of lunar terrain surface. A linear pose estimation algorithm with multi matching features is introduced, which verified the feasibility of feature matching initialization algorithm on modeled lunar terrain surface.Then we presented a multi-sensor data fusion based lunar surface hazard detection methods. On the CCD imaging hazard detection, 2-D Renyi's entropy threshold based rock detection and texture analysis based hazard detection methods are proposed. With an imaging LIDAR model introduced, we presented a LIDAR simulation methods and local surface gradient is calculated with LIDAR data to verify the safety of spacecraft's landing. Then a fuzzy logic based fission decision algorithm for CCD and LIDAR information is developed, which solves the problem of lunar surface safety evaluation under multi-sensor condition.Finally, on the research of lunar surface feature matching motion estimation and relative nonlinear filters, we introduce a vision/inertial navigation system which estimated the position and velocity quite well, but the attitude estimation results are not ideal. To expedite the convergence rate and reduce the errors of attitude estimation, a federated UKF VNS/SINS/CNS filter is proposed. Simulations of federated filter illustrate the improvement of attitude estimation.