论文标题
NDD：基于循环闭合检测的正态分布的3D点云描述符
NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection
论文作者
论文摘要
循环封闭检测是在复杂环境中长期机器人导航的关键技术。在本文中，我们提出了一个全局描述符，称为正态分布描述符（NDD），用于3D点云循环闭合检测。描述符编码点云作为描述符的概率密度分数和熵。我们还提出了快速旋转对准过程，并将相关系数用作描述符之间的相似性。实验结果表明，我们的方法在准确性和效率上都优于最新点云描述符。源代码可用，并且可以集成到现有的LiDAR进音表和映射（BOAM）系统中。
Loop closure detection is a key technology for long-term robot navigation in complex environments. In this paper, we present a global descriptor, named Normal Distribution Descriptor (NDD), for 3D point cloud loop closure detection. The descriptor encodes both the probability density score and entropy of a point cloud as the descriptor. We also propose a fast rotation alignment process and use correlation coefficient as the similarity between descriptors. Experimental results show that our approach outperforms the state-of-the-art point cloud descriptors in both accuracy and efficency. The source code is available and can be integrated into existing LiDAR odometry and mapping (LOAM) systems.
