KDD-LOAM：联合学习的关键点检测器与描述子辅助的激光雷达里程计与建图
KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping
AI总结：
提出紧耦合关键点检测器与描述子TCKDD及辅助框架KDD-LOAM，通过多任务全卷积网络与自监督损失提升点云配准性能，在KITTI上显著超越LOAM。
摘要：
基于独特3D特征表示的稀疏关键点匹配能够提升点云配准的效率和鲁棒性。现有的基于学习的3D描述子和关键点检测器要么是独立的，要么是松耦合的，因此它们无法完全相互适应。在本研究中，我们提出了一种基于多任务全卷积网络和概率检测损失的紧耦合关键点检测器与描述子（TCKDD）。具体而言，这种自监督检测损失使关键点检测器能够完全适应任何联合学习的描述子，并有助于描述子的自监督学习。在室内和室外数据集上的大量实验表明，我们的TCKDD在点云配准中达到了最先进的性能。此外，我们设计了一个关键点检测器与描述子辅助的激光雷达里程计与建图框架（KDD-LOAM），其实时里程计依赖于基于关键点描述子匹配的RANSAC。稀疏关键点进一步用于高效的扫描到地图配准与建图。在KITTI数据集上的实验表明，KDD-LOAM显著超越了LOAM，并在里程计方面展现出极具竞争力的性能。
英文摘要：
Sparse keypoint matching based on distinct 3D feature representations can improve the efficiency and robustness of point cloud registration. Existing learning-based 3D descriptors and keypoint detectors are either independent or loosely coupled, so they cannot fully adapt to each other. In this work, we propose a tightly coupled keypoint detector and descriptor (TCKDD) based on a multi-task fully convolutional network with a probabilistic detection loss. In particular, this self-supervised detection loss fully adapts the keypoint detector to any jointly learned descriptors and benefits the self-supervised learning of descriptors. Extensive experiments on both indoor and outdoor datasets show that our TCKDD achieves state-of-the-art performance in point cloud registration. Furthermore, we design a keypoint detector and descriptors-assisted LiDAR odometry and mapping framework (KDD-LOAM), whose real-time odometry relies on keypoint descriptor matching-based RANSAC. The sparse keypoints are further used for efficient scan-to-map registration and mapping. Experiments on KITTI dataset demonstrate that KDD-LOAM significantly surpasses LOAM and shows competitive performance in odometry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
