Landing exploration of extraterrestrial objects is an important form and method of deep space exploration in China. To extract the pose of the probe rover in the ground exploration simulation training system, an approach of combining laser scanner and target ball is proposed to fit the position and attitude data of the rover, and the ground simulation training system is established. First of all, the laser scanning data is preprocessed to remove the outliers of the point cloud, and then the appropriate parameters are selected for resampling the data. The point cloud is segmented using the improved region growth method based on classification, a set of prespecified number of points are adopted to fit multiple target spheres and a local coordinate system is established. Through the analysis of the experimental data, the fitting accuracy of the target sphere meets the maximum allowable error of 3mm, and the point cloud processing speed is effectively improved, which verifies the accuracy and efficiency of the feature recognition method. Finally, the position and the attitude matrix of the rover is obtained through coordinate transformation to calibrate the camera accuracy.