This paper proposes a 3D LiDAR SLAM method that improves accuracy, robustness and computational efficiency for iterative closest point (ICP) employed locally approximated geometry with clusters of normal distributions. In comparison with previous normal distribution based ICP methods, such as NDT and GICP, our ICP method is simply stabilized with normalization of the cost function by Frobenius norm and regulalization of covariance matrix. The previous methods are stabilized with pricipal component analysis (PCA), whose computational cost is higher than our method. Moreover, our SLAM method can reduce the effect of the wrong loop closure constraints. Exeprimental results show that our SLAM method has advantages against open source state-of-the-art methods that are LOAM, LeGO-LOAM and hdl graph slam.