Chen Maolin, Wang Siying, Wang Mingwei, Wan Youchuan, He Peipei
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Jiangsu Hi-Target Marine Technology Co., Ltd., Nanjin 210032, China.
Sensors (Basel). 2017 Jan 20;17(1):197. doi: 10.3390/s17010197.
Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency.
地面激光扫描点云的自动配准是一个关键但尚未解决的课题，在许多领域都备受关注。本研究将地面激光扫描仪与智能手机相结合，用于对具有小横滚角、俯仰角和高度差的水平点云进行粗配准，这是一种用于地面激光扫描的新型传感器组合模式。首先利用智能手机的GPS坐标计算相邻两个扫描位置之间的近似距离。然后，使用二维分布熵来度量两次扫描之间的分布一致性，并搜索最优的初始变换参数。为此，我们提出了一种称为迭代最小熵（IME）的方法，基于两个准则来校正初始变换参数：平均熵与最小熵之间的差值以及从最小熵到期望熵的偏差。最后，使用包含来自全景和非全景、植被主导和建筑主导案例的数千万个点的两个数据集对所提出的方法进行评估，该方法能够实现高精度和高效率。
