LiDAR-OdomNet: LiDAR Odometry Network Using Feature Fusion Based on Attention

LiDAR odometry is an important problem for autonomous vehicles, robotics, drones, 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 compared to current methods. An ablation study has been done using experiments to determine the significance of the proposed approach. We have analyzed every parameter of the pose matrix and plotted the results. To check LiDAR-OdomNet generalization, we have looked at some samples from the TIAND dataset and analyzed the results. Our code is available at: https://github.com/ParvezAlam123/LiDAR-OdomNet