The traditional fast LiDAR odometry and mapping （F-LOAM） algorithm performs a two-stage de-distortion process on the feature points， but only the first stage de-distorts the feature points， and the second-stage de-distortion is used for building the map， which leads to the lack of accuracy in the bit-position estimation. In order to solve this problem， this paper proposes an improved three-stage de-distortion mechanism combined with a voxelized grid-based hierarchical downsampling mechanism to improve the real-time performance of the algorithm. The improved F-LOAM algorithm shows excellent test results on the KITTI dataset. The three-stage de-distortion mechanism and the hierarchical downsampling strategy not only reduce the computational burden effectively， but also ensure the validity of feature points and the accuracy of the global map.