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.

稀疏关键点匹配改善了点云配准的效率和稳健性。该研究提出了一种基于多任务全卷积网络和概率检测损失的紧密耦合的关键点检测器和描述子 (TCKDD)，并通过自监督学习实现描述子的全面自适应及关键点检测器的优化。在室内和室外数据集上的大量实验证明，TCKDD 在点云配准中取得了最先进的性能。此外，我们设计了一个关键点检测器和辅助 LiDAR 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至地图配准和建图。在 KITTI 数据集上的实验证明，KDD-LOAM 明显优于 LOAM，在里程计方面表现出有竞争力的性能。