Registration of large-scale outdoor Terrestrial Laser Scanning (TLS) point clouds remains many challenges in the scenes with symmetric and repetitive elements (e.g., park, forest, and tunnel), the weak geometric features (e.g., underground excavation), and dramatically changes in different phases (e.g., mountain). To address these issues, a novel neural network JoKDNet is proposed to jointly learn the keypoint detection and feature description to improve the feasibility and accuracy of point clouds registration. Firstly, a novel keypoint detection module is introduced to automatically learn the score of each sampled point and regard the most significant Top-k sampled points as the detected keypoints. Secondly, an enhanced feature description module is proposed to learn the feature representation of each keypoint by fusing the hierarchical local features and context features. Thirdly, a loss function is designed to make the detected keypoints more distinguishable for matching, which simultaneously maximizes the feature distance between non-corresponding keypoints and minimizes the feature distance of corresponding keypoints. Finally, the distance matrix module and RANdom SAmple Consensus (RANSAC) are utilized to determine the correspondences of source and target point clouds for the transformation calculation. Comprehensive experiments show that the JoKDNet performs effectively on five challenging scenes (e.g., park, forest, tunnel, underground excavation, and mountain) from two datasets (WHU-TLS and ETH-TLS) in terms of registration errors, and robustness to varying scenes, with the maximum rotation error less than 0.06° and maximum translation error less than 0.84 m without ICP.

在要素对称重复的场景（如公园、森林、隧道）、弱几何特征（如地下开挖）和剧烈变化的场景中，大规模室外地面激光扫描（TLS）点云的配准仍然存在许多挑战在不同的阶段（例如，山）。为了解决这些问题，提出了一种新颖的神经网络 JoKDNet 来联合学习关键点检测和特征描述，以提高点云配准的可行性和准确性。首先，引入了一种新颖的关键点检测模块来自动学习每个采样点的得分，并将最重要的Top-k采样点作为检测到的关键点。其次，提出了一个增强的特征描述模块，通过融合层次局部特征和上下文特征来学习每个关键点的特征表示。第三，设计了一个损失函数，使检测到的关键点更容易区分以进行匹配，同时最大化非对应关键点之间的特征距离，同时最小化对应关键点的特征距离。最后，利用距离矩阵模块和随机样本共识（RANSAC）来确定源点云和目标点云的对应关系，以进行变换计算。综合实验表明，就注册错误而言，JoKDNet 在来自两个数据集（WHU-TLS 和 ETH-TLS）的五个具有挑战性的场景（例如，公园、森林、隧道、地下开挖和山区）上有效执行，