  We investigate a new paradigm that uses differentiable SLAM architectures in
a self-supervised manner to train end-to-end deep learning models in various
LiDAR based applications. To the best of our knowledge there does not exist any
work that leverages SLAM as a training signal for deep learning based models.
We explore new ways to improve the efficiency, robustness, and adaptability of
LiDAR systems with deep learning techniques. We focus on the potential benefits
of differentiable SLAM architectures for improving performance of deep learning
tasks such as classification, regression as well as SLAM. Our experimental
results demonstrate a non-trivial increase in the performance of two deep
learning applications - Ground Level Estimation and Dynamic to Static LiDAR
Translation, when used with differentiable SLAM architectures. Overall, our
findings provide important insights that enhance the performance of LiDAR based
navigation systems. We demonstrate that this new paradigm of using SLAM Loss
signal while training LiDAR based models can be easily adopted by the
community.
