点云
人工智能
计算机科学
里程计
熵(时间箭头)
计算机视觉
同时定位和映射
相似性(几何)
模式识别(心理学)
机器人
图像(数学)
移动机器人
物理
量子力学
作者
Ruihao Zhou,He Li,Hong Zhang,Xubin Lin,Yisheng Guan
标识
DOI:10.1109/iros47612.2022.9981180
摘要
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.
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