Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D or 3D object detection, recognition, and registration. As is known, 2D feature extraction and matching have already achieved great success. Unfortunately, in the field of 3D, the current methods may fail to support the extensive application of 3D LiDAR sensors in robotic vision tasks due to their poor descriptiveness and inefficiency. To address this limitation, we propose a novel 3D feature representation method: Linear Keypoints representation for 3D LiDAR point cloud, called LinK3D. The novelty of LinK3D lies in that it fully considers the characteristics (such as the sparsity and complexity) of LiDAR point clouds and represents the keypoint with its robust neighbor keypoints, which provide strong constraints in the description of the keypoint. The proposed LinK3D has been evaluated on three public datasets, and the experimental results show that our method achieves great matching performance. More importantly, LinK3D also shows excellent real-time performance, faster than the sensor frame rate at 10 Hz of a typical rotating LiDAR sensor. LinK3D only takes an average of 30 milliseconds to extract features from the point cloud collected by a 64-beam LiDAR and takes merely about 20 milliseconds to match two LiDAR scans when executed on a computer with an Intel Core i7 processor. Moreover, our method can be extended to LiDAR odometry task, and shows good scalability. We release the implementation of our method at https://github.com/YungeCui/LinK3D.

翻译：特征提取与匹配是许多机器人视觉任务的基础环节，例如2D或3D目标检测、识别与配准。众所周知，2D特征提取与匹配已取得巨大成功。然而在3D领域，现有方法因描述性差、效率低下等问题，难以支撑3D激光雷达传感器在机器人视觉任务中的广泛应用。针对这一局限，我们提出一种新型3D特征表征方法——面向3D激光雷达点云的线性关键点表征方法（LinK3D）。LinK3D的创新之处在于：该方法充分考量激光雷达点云的特性（如稀疏性与复杂性），通过鲁棒的邻域关键点来表征目标关键点，从而为关键点描述提供强约束条件。我们在三个公开数据集上对LinK3D进行了评估，实验结果表明该方法展现出优异的匹配性能。更重要的是，LinK3D还表现出卓越的实时性能——其处理速度超过典型旋转式激光雷达传感器10Hz的帧率。在搭载Intel Core i7处理器的计算机上，LinK3D从64线激光雷达采集的点云中提取特征仅需平均30毫秒，完成两帧激光雷达扫描的匹配仅需约20毫秒。此外，我们的方法可扩展应用于激光雷达里程计任务，展现出良好的可扩展性。本方法的实现代码已开源至https://github.com/YungeCui/LinK3D。