基于改进正态分布变换算法的点云配准

Point Cloud Registration Based on Improved Normal Distribution Transform Algorithm

正态分布变换(NDT)算法是一种应用在同时定位和地图生成(SLAM)中的点云配准算法。针对地面激光扫描(TLS)数据的特点,改进了NDT算法,提出了一种基于SURF的NDT配准算法,使之能应用在TLS中。该算法首先建立点云和图像间的映射关系把点云影像化;利用加速稳健特征(SURF)算法提取图像的特征点并找出特征点对;根据映射关系找到相应的三维特征匹配点,求出变换矩阵,完成点云初始配准。在NDT算法中,设置初始矩阵为单位矩阵,对点云体素化并使用概率分布函数对点云精细配准。实验结果证明,该算法不但适用于地面激光数据的配准,且其配准精度高、运算时间少,尤其对于不同分辨率的点云有良好的配准效果。

Normal distribution transform （NDT） algorithm is a point cloud registration algorithm applied in simultaneous localization and mapping （SLAM）. According to the characteristics of terrestrial laser scanning （TLS） technique, we propose an improved NDT algorithm based on speeded up robust feature （SURF） algorithm so that it can be applied conveniently in TLS. In this algorithm, firstly the corresponding relation between the point cloud and the image is created for the point cloud visualization; the feature points are extracted from the image by using SURF algorithm and the matching feature points are identified; according to the corresponding relation, the transformation matrix is calculated, and the initial registration of point clouds is completed. In the NDT, the initial matrix is set as a unit matrix, and the point clouds are divided into three-dimensional voxel grids and registered precisely by the probability distribution function. The experimental results show that this algorithm is not only applicable to the registration for TLS, but also exhibits higher registration accuracy and less calculating time, and it has especially a good registration effect for the point clouds with different resolutions.