LLS-SMGSC: A Lidar Localization System Based on Simplified Maps Integrated with Global Search Capabilities

To address the issues of large storage requirements in maps and the dependence of localization accuracy on initial pose estimation, this paper proposes a novel relocalization method named LLS-SMGSC, which is based on simplified maps integrated with Global Search capabilities. Firstly, we partition the map-based on grid size to reduce memory usage. Next, we voxelize the point cloud and map and extract surfel. Then, a coarse-to-fine hierarchical alignment module between the initial frame and maps to estimate the initial global pose. Finally, unmanned platform pose is estimated by the Normal Distribution Transform (ndt) algorithm. Experiments demonstrate that LLS-SMGSC achieves the highest localization accuracy in both unstructured and structured environments while maintaining computational efficiency.