To extend the proposed approach to handle translational misalignment, the multi-task learning framework can be modified to include additional outputs that predict translational offsets alongside the existing rotational parameters (roll, pitch, yaw). This can be achieved by integrating a translational prediction head into the existing network architecture, which would output three additional scalar values representing the translational shifts along the x, y, and z axes.
The training process would need to incorporate synthetic data augmentation techniques that simulate translational misalignments, allowing the model to learn the relationship between the sensor data and the corresponding ground truth translations. By perturbing the LiDAR points in the camera frame of reference with known translational offsets during training, the model can learn to predict these offsets effectively.
Furthermore, the loss functions would need to be adjusted to include a translational misalignment loss, ensuring that the model is penalized for inaccuracies in both rotational and translational predictions. This comprehensive approach would enhance the robustness of the perception system against a wider range of sensor misalignments, ultimately improving the accuracy of object detection and tracking in autonomous vehicles.