同步建图与定位（SLAM）可实现月球车在未知复杂月面环境下的定位与导航，月球表面由陨坑、石头等起伏地形构成，缺乏树木、建筑物等地面存有的显著特征，大量特征不显著的点云数据会对月球车定位精度和实时性造成影响。本文提出了一种针对月面环境的显著特征点云提取方法以及基于曲面定位能力估计的增量式优化算法，通过Fisher信息矩阵计算曲面定位能力指标，获取机器人位姿估计的不确定性测量，利用增量式的SLAM方案进行优化，用于提高定位精度与实时性。通过在Gazebo （物理仿真平台）仿真场景下的测试，验证了算法性能。
Simultaneous Localization And Mapping (SLAM) can realize the localization and navigation of the lunar rover in the unknown complex lunar environment. The lunar surface is composed of undulating terrain such as craters and stones, lacking the salient features of ground such as trees and buildings. Point cloud data with insignificant features will affect the localization accuracy and real-time performance of the lunar rover. This paper proposes a method to extract salient feature point clouds for the lunar surface environment and an incremental optimization algorithm based on the curve localizability estimation. The information matrix calculates the curve localizability index, obtains the uncertainty measurement of the robot pose estimation, and uses the incremental SLAM scheme for optimization to improve the positioning accuracy and real-timeness. The performance of the algorithm is verified by testing in Gazebo (physical simulation platform) simulation scenario.
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