This paper presents a comprehensive inertial aided 3D LiDAR SLAM system with hybrid geometric primitives in large-scale environments, including a tightly-coupled LiDAR-Inertial-Odometry (LIO), a global mapping module supported by learning-based loop closure detection and a sub-maps matching algorithm. An efficient method is developed to simultaneously extract explicit plane features and point features from each raw point cloud. To make full use of the structural information of the surroundings, plane features and point features (ground and edge) are tracked across a fix-sized group of LiDAR keyframes in the local map. For effective loop closure detection in large-scale environments, we integrate the learning-based point cloud network and a keyframe sequence matching method to detect loops. Finally, a novel, deterministic and near real-time plane-driven sub-maps matching algorithm is proposed to close the loops. The proposed SLAM system is validated with experiments on different types of environments.