Rapid SLAM Method for Star Surface Rover in Unstructured Space Environments

The space environment is characterized by unstructured features, sparsity, and poor lighting conditions. The difficulty in extracting features from the visual frontend of traditional SLAM methods results in poor localization and time-consuming issues. This paper proposes a rapid and real-time localization and mapping method for star chart surveyors in unstructured space environments. Improved localization is achieved using multiple sensor fusion to sense the space environment. We replaced the traditional feature extraction module with an enhanced SuperPoint feature extraction network to tackle the challenge of challenging feature extraction in unstructured space environments. By dynamically adjusting detection thresholds, we achieved uniform detection and description of image keypoints, ultimately resulting in robust and accurate feature association information. Furthermore, we minimized redundant information to achieve precise positioning with high efficiency and low power consumption. We established a star surface rover simulation system and created simulated environments resembling Mars and the lunar surface. Compared to the LVI-SAM system, our method achieved a 20% improvement in localization accuracy for lunar scenarios. In Mars scenarios, our method achieved a positioning accuracy of 0.716 m and reduced runtime by 18.682 s for the same tasks. Our approach exhibits higher localization accuracy and lower power consumption in unstructured space environments.