A Federated Deep Unrolling Method for Lidar Super-Resolution: Benefits in SLAM.

In this paper, we propose a novel federated deep unrolling method for increasing the accuracy of the Lidar Super resolution. The proposed framework not only offers notable improvements in Lidar-based SLAM methodologies but also provides a solution to the significant cost associated with high-resolution Lidar sensors. Particularly, our method can be adopted by a number of vehicles coordinated with a server towards learning a regularizer - a neural network - for capturing the dependencies of the Lidar data. To tackle this adaptive federated optimization problem effectively, we initially propose a deep unrolling framework, converting our solution into a well-justified deep learning architecture. The learnable parameters of this architecture are directly derived from the solution of the proposed optimization problem, thus resulting in an explainable architecture. Further, we extend the capabilities of our deep unrolling technique by incorporating a federated learning strategy. Our federated deep unrolling model employs an innovative Adapt-then-Combine strategy, where each vehicle optimizes its model and, subsequently, their learnable regularizers are combined to formulate a robust global regularizer, equipped to handle diverse environmental conditions. Through extensive numerical evaluations on real-world Lidar based SLAM applications, our proposed model demonstrates superior performance along with a significant reduction in trainable parameters, with 99.75% fewer parameters compared to state of the art lidar super-resolution deep neural networks. Essentially, this study is the first initiative to combine deep unrolling with federated learning, showcasing an efficient, and data-secure approach to automotive Lidar super-resolution SLAM applications. The source code can be found at: https://github.com/alexandrosgk/Federated-Deep-Unrolling-Lidar-Super-resolution-SLAM.git .