LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cannot produce satisfactory results on large-scale datasets with thousands of frames. This is due to the lack of loop closures and exact cross-frame point correspondences, and the slow convergence of its global localization network. We propose DeepMapping2 by adding two novel techniques to address these issues: (1) organization of training batch based on map topology from loop closing, and (2) self-supervised local-to-global point consistency loss leveraging pairwise registration. Our experiments and ablation studies on public datasets (KITTI, NCLT, and Nebula) demonstrate the effectiveness of our method.
@article{arxiv.2212.06331,
title = {DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization},
author = {Chao Chen and Xinhao Liu and Yiming Li and Li Ding and Chen Feng},
journal= {arXiv preprint arXiv:2212.06331},
year = {2023}
}