L-DYNO: Framework to Learn Consistent Visual Features Using Robot’s Motion.

This is an official implementation of our work published in ICRA'24. The paper utilizes the robot's transformations through an external signal (inertial sensing, for example) and gives attention to image space that is most consistent with the external signal.

Datasets:

KITTI: Download the dataset (grayscale images) from here and prepare the KITTI folder as specified.

F1tenth Dataset: [TODO]

Training:

train.py

We train the pipeline end-to-end on the RTX A100.

Mask Generation:

viz_masked.py

To test the generated masks on trajectory estimation and reprojection errors follow this repo.

link.mp4