不同扫描几何点云的自动粗配准：检测器与描述符测试
Automatic coarse co-registration of point clouds from diverse scan geometries: a test of detectors and descriptors
AI总结：
本文针对无人机与自行车不同视角点云的自动粗配准，测试四种关键点检测器和三种描述符，发现 NARF 关键点更多、速度更快且精度相当。
摘要：
如今，点云可由大量传感器采集，其中一些精度更高、成本也更高，另一些精度较低但成本也较低。不仅不同传感器的选择很多，而且这些传感器还可由不同平台搭载，从而提供不同的扫描几何形态。在本研究中，我们测试了四种不同关键点检测器和三种特征描述符的提取效果。我们以计算时间为指标对其性能进行基准测试，并评估它们在两朵点云的自动粗配准中的精度表现；这两朵点云由不同传感器、平台和扫描几何采集。其中一朵被定义为具有较高精度，因此用作参考，它通过搭载 Riegl MiniVUX-3 的无人机飞行测量获得；另一朵则是在地面不平的步行路径上，由自行车搭载 Livox Horizon 采集。本研究的创新之处在于比较多种策略，用于快速配准来自差异极大测量几何的点云，因为无人机具有鸟瞰视角，而自行车具有地面视角。另一个挑战与自行车传感器组合成本较低有关，再加上地形粗糙，合理地导致测量精度较低。核心思想是利用距离图像捕捉被测区域几何形态的简化版本，然后寻找最佳特征以匹配关键点。结果表明，在该场景中，NARF 特征检测到更多关键点，并带来更快的配准过程；与此同时，其配准精度与所有关键点检测器和特征组合的结果相近。
英文摘要：
Point clouds are collected nowadays from a plethora of sensors, some having higher accuracies and higher costs, some having lower accuracies but also lower costs. Not only there is a large choice for different sensors, but also these can be transported by different platforms, which can provide different scan geometries. In this work we test the extraction of four different keypoint detectors and three feature descriptors. We benchmark performance in terms of calculation time and we assess their performance in terms of accuracy in their ability in coarse automatic co-registration of two clouds that are collected with different sensors, platforms and scan geometries. One, which we define as having the higher accuracy, and thus will be used as reference, was surveyed via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox Horizon over a walking path with un-even ground.The novelty in this work consists in comparing several strategies for fast alignment of point clouds from very different surveying geometries, as the drone has a bird's eye view and the bicycle a ground-based view. An added challenge is related to the lower cost of the bicycle sensor ensemble that, together with the rough terrain, reasonably results in lower accuracy of the survey. The main idea is to use range images to capture a simplified version of the geometry of the surveyed area and then find the best features to match keypoints. Results show that NARF features detected more keypoints and resulted in a faster co-registration procedure in this scenariowhereas the accuracy of the co-registration is similar to all the combinations of keypoint detectors and features.
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。
