We present a registration strategy based on a planar primitive group for indoor environments between a large-scale point cloud and a small-scale point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm first divides the point cloud into planes by region growing and then refines the plane boundaries by local $k$ -means clustering. The planes are grouped to obtain planar primitive groups that are used to infer potential matching regions for an effective coarse registration and then fine-tuning. The algorithm is applied for autonomous vehicle localization in underground garages. Evaluation on four datasets shows that the algorithm can provide decimeter-level localization accuracy in seconds.