论文标题
DeepMapping2：自我监管的大规模激光雷达地图优化
DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization
论文作者
论文摘要
LIDAR映射在自动驾驶和移动机器人技术中既重要却又具有挑战性。为了解决这样的全球点云注册问题，DeepMapping将复杂的地图估计转换为对简单深网的自学训练。尽管在小型数据集上具有广泛的收敛范围，但DeepMapping仍无法在具有数千帧的大规模数据集上产生令人满意的结果。这是由于缺乏循环封闭和精确的跨帧点对应关系以及其全球本地化网络的慢速收敛性。我们通过添加两种新型技术来解决这些问题，提出了DeepMapping2：（1）基于循环结束的地图拓扑的培训批次组织，以及（2）自我监督的本地到全球点一致性损失损失对成对的注册。我们对公共数据集（Kitti，NCLT和Nebula）的实验和消融研究证明了我们方法的有效性。
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.
