A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based methods achieve this goal by using only the geometric information, disregarding the semantics of the scene. In this work, we introduce PADLoC for joint loop closure detection and registration in LiDAR-based SLAM frameworks. We propose a novel transformer-based head for point cloud matching and registration, and to leverage panoptic information during training time. In particular, we propose a novel loss function that reframes the matching problem as a classification task for the semantic labels and as a graph connectivity assignment for the instance labels. During inference, PADLoC does not require panoptic annotations, making it more versatile than other methods. Additionally, we show that using two shared matching and registration heads with their source and target inputs swapped increases the overall performance by enforcing forward-backward consistency. We perform extensive evaluations of PADLoC on multiple real-world datasets demonstrating that it achieves state-of-the-art results. The code of our work is publicly available at http://padloc.cs.uni-freiburg.de.

翻译：基于图的SLAM系统的关键组成部分是能够在轨迹中检测闭环，以减少里程计随时间累积的漂移。大多数基于激光雷达的方法仅利用几何信息实现这一目标，忽略了场景的语义。本文提出PADLoC，用于激光雷达SLAM框架中的联合闭环检测与配准。我们提出一种新颖的基于Transformer的点云匹配与配准头部结构，并在训练阶段利用全景信息。特别地，我们提出一种新颖的损失函数，将匹配问题重新定义为语义标签的分类任务和实例标签的图连通性分配任务。在推理阶段，PADLoC不需要全景标注，使其比其他方法更具通用性。此外，我们证明使用两个共享的匹配与配准头部并将源输入和目标输入互换，可以通过强制执行前向-后向一致性来提升整体性能。我们在多个真实世界数据集上对PADLoC进行了广泛评估，表明其达到了最先进的结果。我们的代码已公开于http://padloc.cs.uni-freiburg.de。