An Improved EKF-SLAM for Mars Surface Exploration

Abstract

In the traditional EKF-SLAM algorithm, the computational complexity and uncertainty will grow up rapidly with the increase of the feature points and the enlargement of the map coverage. As we know, the computational complexity is proportional to the quadratic of the number of feature points contained in a single filtering process. The approach represented in the paper combines EKF-SLAM with local submaps, which can improve the computational efficiency and reduce the computational complexity. At first, an independent local submap is established for the observed feature points. When the number of feature points contained in the local submap reaches a certain threshold value, the local submap is integrated into the global map. At last, the submap is initialized again. The simulation results show that the approach can reduce the computational complexity effectively and increase the computation speed greatly in the case of maintaining the computational accuracy of the traditional EKF-SLAM algorithm.