This application relates to the fields of positioning, navigation, semantic segmentation, and target detection technologies, and particularly to a method and
system for collaborative positioning of astronauts and scientific targets on the lunar surface. The method includes: constructing a feature point map by collecting visual and inertial data, and using VIO sliding window optimization to estimate the astronaut's
pose, generating a 3D environmental
point cloud; further performing 3D target detection on the
point cloud, distinguishing between scientific targets and lunar
base station objects, and using
prior information of scientific targets and a random sampling
consensus algorithm for screening and constraint, constructing geometric constraints by fitting a geometric model of the targets and feeding them back into the
pose optimization process, introducing iterative optimization to achieve
pose correction; simultaneously calculating the relative pose of the astronaut and each target, and updating the environmental map. This method improves positioning accuracy and robustness in the absence of GPS and is suitable for autonomous navigation and mission execution in the complex lunar environment.