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.

翻译：我们研究了一种新范式，即利用可微分SLAM架构以自监督方式训练面向多种激光雷达应用的端到端深度学习模型。据我们所知，目前尚未有研究将SLAM作为深度学习模型的训练信号。我们探索了通过深度学习技术提升激光雷达系统效率、鲁棒性和适应性的新途径，重点关注可微分SLAM架构在改进分类、回归及SLAM等深度学习任务性能方面的潜在优势。实验结果表明，将可微分SLAM架构用于地面高度估计与动态到静态激光雷达转换这两项深度学习应用时，其性能获得显著提升。总体而言，我们的研究为提升基于激光雷达的导航系统性能提供了重要启示，并证明在训练激光雷达模型时采用SLAM损失信号这一新范式可被学界便捷采纳。