LiDAR odometry is an important problem for au- tonomous vehicles, robotics, drone, etc. This paper proposes a data-driven deep learning-based LiDAR odometry network LiDAR-OdomNet (LiDAR Odometry Network). The network has been trained on the KITTI odometry benchmark. It predicts translation parameters of the pose matrix with 0.0919 RMSE value which is the minimum error obtained as compared to state-of-the-art methods. An ablation study has been done using experiments for the importance of the proposed method. We have analyzed every parameter of the pose matrix and plotted the results.